A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few ruless this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a specific target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users' navigation is used to extract popular web paths and predict current users' next steps. Such purely usage-based probabilistic models, however, present certain shortcomings. Since the prediction of users' navigational behaviour is based solely on the usage data, structural properties of the Web graph are ignored. Thus important - in terms of pagerank authority score - paths may be underrated. In this paper we present a hybrid probabilistic predictive model extending the properties of Markov models by incorporating link analysis methods. More specifically, we propose the use of a PageRank-style algorithm for assigning prior probabilities to the web pages based on their importance in the web site's graph. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches. <s> BIB003 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Web personalization is the process of customizing a web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user’s navigational behavior. Personalized recommendation by predicting user-browsing behavior using association-mining technology has gained much attention in web personalization research area. However, the resulting association patterns did not perform well in prediction of future browsing patterns due to the low matching rate of the resulting rules and users’ browsing behavior. In this paper, we extend the traditional association rule problem by allowing a weight to be associated with each item in a transaction to reflect the interest/intensity of each item within the transaction. In turn, this provides us with an opportunity to associate a weight parameter with each item in a resulting association rule. We assign a significant weight to each page based on the time spent by user on each page and visiting frequency of each page, taking in to account the degree of interest instead of binary weighting. We present new personalized recommendation method base on the proposed weighted association-mining technique. We show, through experimentation on real data set that this approach results in more objective and representative predictions and shows a significant improvement in the recommendation effectiveness in comparison to the traditional association rule approaches. <s> BIB004 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user. In this paper, we present a method bringing both approaches together. Our method is based on personalized transition graphs over underlying Markov chains. That means for each user an own transition matrix is learned - thus in total the method uses a transition cube. As the observations for estimating the transitions are usually very limited, our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition. We show that our factorized personalized MC (FPMC) model subsumes both a common Markov chain and the normal matrix factorization model. For learning the model parameters, we introduce an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data. Empirically, we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization. <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and the new-item problem, while retaining its advantages, in particular speed and predictive accuracy. We use the mapping concept to construct an attribute-aware matrix factorization model for item recommendation from implicit, positive-only feedback. Experiments on the new-item problem show that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor. <s> BIB006 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem. <s> BIB007 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressively querying user responses through an initial interview process has been proposed as a useful new user preference elicitation strategy. In this paper, we present functional matrix factorization (fMF), a novel cold-start recommendation method that solves the problem of initial interview construction within the context of learning user and item profiles. Specifically, fMF constructs a decision tree for the initial interview with each node being an interview question, enabling the recommender to query a user adaptively according to her prior responses. More importantly, we associate latent profiles for each node of the tree --- in effect restricting the latent profiles to be a function of possible answers to the interview questions --- which allows the profiles to be gradually refined through the interview process based on user responses. We develop an iterative optimization algorithm that alternates between decision tree construction and latent profiles extraction as well as a regularization scheme that takes into account of the tree structure. Experimental results on three benchmark recommendation data sets demonstrate that the proposed fMF algorithm significantly outperforms existing methods for cold-start recommendation. <s> BIB008 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement. <s> BIB009 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Financial variables such as asset returns in the massive market contain various hierarchical and horizontal relationships forming complicated dependence structures. Modeling and mining of these structures is challenging due to their own high structural complexities as well as the stylized facts of the market data. This paper introduces a new canonical vine dependence model to identify the asymmetric and non-linear dependence structures of asset returns without any prior independence assumptions. To simplify the model while maintaining its merit, a partial correlation based method is proposed to optimize the canonical vine. Compared with the original canonical vine, the new model can still maintain the most important dependence but many unimportant nodes are removed to simplify the canonical vine structure. Our model is applied to construct and analyze dependence structures of European stocks as case studies. Its performance is evaluated by measuring portfolio of Value at Risk, a widely used risk management measure. In comparison to a very recent canonical vine model and the 'full' model, our experimental results demonstrate that our model has a much better quality of Value at Risk, providing insightful knowledge for investors to control and reduce the aggregation risk of the portfolio. <s> BIB010 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Based on the intuition that frequent patterns can be used to predict the next few items that users would want to access, sequential pattern mining-based next-items recommendation algorithms have performed well in empirical studies including online product recommendation. However, most current methods do not perform personalized sequential pattern mining, and this seriously limits their capability to recommend the best next-items to each specific target user. In this paper, we introduce a personalized sequential pattern mining-based recommendation framework. Using a novel Competence Score measure, the proposed framework effectively learns user-specific sequence importance knowledge, and exploits this additional knowledge for accurate personalized recommendation. Experimental results on real-world datasets demonstrate that the proposed framework effectively improves the efficiency for mining sequential patterns, increases the user-relevance of the identified frequent patterns, and most importantly, generates significantly more accurate next-items recommendation for the target users. <s> BIB011 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Typical recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential optimization problem and, consequently, that Markov decision processes (MDPs) provide a more appropriate model for recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation and the expected value of each recommendation. To succeed in practice, an MDP-based recommender system must employ a strong initial model, must be solvable quickly, and should not consume too much memory. In this paper, we describe our particular MDP model, its initialization using a predictive model, the solution and update algorithm, and its actual performance on a commercial site. We also describe the particular predictive model we used which outperforms previous models. Our system is one of a small number of commercially deployed recommender systems. As far as we know, it is the first to report experimental analysis conducted on a real commercial site. These results validate the commercial value of recommender systems, and in particular, of our MDP-based approach. <s> BIB012 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Interdisciplinary collaborations have generated huge impact to society. However, it is often hard for researchers to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations? Cross-domain collaborations exhibit very different patterns compared to traditional collaborations in the same domain: 1) sparse connection: cross-domain collaborations are rare; 2) complementary expertise: cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems. In this paper, we analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning (CTL) model to address these challenges. For handling sparse connections, CTL consolidates the existing cross-domain collaborations through topic layers instead of at author layers, which alleviates the sparseness issue. For handling complementary expertise, CTL models topic distributions from source and target domains separately, as well as the correlation across domains. For handling topic skewness, CTL only models relevant topics to the cross-domain collaboration. We compare CTL with several baseline approaches on large publication datasets from different domains. CTL outperforms baselines significantly on multiple recommendation metrics. Beyond accurate recommendation performance, CTL is also insensitive to parameter tuning as confirmed in the sensitivity analysis. <s> BIB013 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Personalized point-of-interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help provide better user experience as well as enable third-party services, e.g., launching advertisements. To provide a good recommendation, various research has been conducted in the literature. However, pervious efforts mainly consider the "check-ins" in a whole and omit their temporal relation. They can only recommend POI globally and cannot know where a user would like to go tomorrow or in the next few days. In this paper, we consider the task of successive personalized POI recommendation in LBSNs, which is a much harder task than standard personalized POI recommendation or prediction. To solve this task, we observe two prominent properties in the check-in sequence: personalized Markov chain and region localization. Hence, we propose a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions. Our proposed FPMC-LR not only exploits the personalized Markov chain in the check-in sequence, but also takes into account users' movement constraint, i.e., moving around a localized region. More importantly, utilizing the information of localized regions, we not only reduce the computation cost largely, but also discard the noisy information to boost recommendation. Results on two real-world LBSNs datasets demonstrate the merits of our proposed FPMC-LR. <s> BIB014 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> With the increasing popularity of Location-based Social Networks, a vast amount of location check-ins have been accumulated. Though location prediction in terms of check-ins has been recently studied, the phenomena that users often check in novel locations has not been addressed. To this end, in this paper, we leveraged collaborative filtering techniques for check-in location prediction and proposed a short- and long-term preference model. We extensively evaluated it on two large-scale check-in datasets from Gowalla and Dianping with 6M and 1M check-ins, respectively, and showed that the proposed model can outperform the competing baselines. <s> BIB015 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> In this paper, we propose Personalized Markov Embedding (PME), a next-song recommendation strategy for online karaoke users. By modeling the sequential singing behavior, we first embed songs and users into a Euclidean space in which distances between songs and users reflect the strength of their relationships. Then, given each user's last song, we can generate personalized recommendations by ranking the candidate songs according to the embedding. Moreover, PME can be trained without any requirement of content information. Finally, we perform an experimental evaluation on a real world data set provided by ihou.com which is an online karaoke website launched by iFLYTEK, and the results clearly demonstrate the effectiveness of PME. <s> BIB016 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models. <s> BIB017 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> One crucial task in recommendation is to predict what a user will buy next given her shopping history. In this paper, we propose a novel neural network to complete this task. The model consists of an embedding layer, a hidden layer and an output layer. Firstly, the distributed representations of the user and the items bought before are obtained and used to form a feature vector by the embedding layer. Then the hidden layer transforms the feature vector to another space by a non-linear operator. Finally, the softmax operator is adopted to output the probabilities of next items. We can see that the model elegantly involves both the user's general interest and the sequential dependencies between items for prediction. Experimental results on two real datasets prove the effectiveness of our model. <s> BIB018 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able to explore the sequential behavior (i.e., buying one item leads to buying another next), as well as account for users' general taste (i.e., what items a user is typically interested in) for recommendation. Moreover, these two factors may interact with each other to influence users' next purchase. To tackle the above problems, in this paper, we introduce a novel recommendation approach, namely hierarchical representation model (HRM). HRM can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction. Meanwhile, the flexibility of applying different aggregation operations, especially nonlinear operations, on representations allows us to model complicated interactions among different factors. Theoretically, we show that our model subsumes several existing methods when choosing proper aggregation operations. Empirically, we demonstrate that our model can consistently outperform the state-of-the-art baselines under different evaluation metrics on real-world transaction data. <s> BIB019 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods. <s> BIB020 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> In the real-world applications, heterogeneous interdependent attributes that consist of both discrete and numerical variables can be observed ubiquitously. The usual representation of these data sets is an information table, assuming the independence of attributes. However, very often, they are actually interdependent on one another, either explicitly or implicitly. Limited research has been conducted in analyzing such attribute interactions, which causes the analysis results to be more local than global. This paper proposes the coupled heterogeneous attribute analysis to capture the interdependence among mixed data by addressing coupling context and coupling weights in unsupervised learning. Such global couplings integrate the interactions within discrete attributes, within numerical attributes and across them to form the coupled representation for mixed-type objects based on dimension conversion and feature selection. This work makes one step forward towards explicitly modeling the interdependence of heterogeneous attributes among mixed data, verified by the applications in data structure analysis, data clustering evaluation, and density comparison. Substantial experiments on 12 UCI data sets show that our approach can effectively capture the global couplings of heterogeneous attributes and outperforms the state-of-the-art methods, supported by statistical analysis. <s> BIB021 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Attribute independence has been taken as a major assumption in the limited research that has been conducted on similarity analysis for categorical data, especially unsupervised learning. However, in real-world data sources, attributes are more or less associated with each other in terms of certain coupling relationships. Accordingly, recent works on attribute dependency aggregation have introduced the co-occurrence of attribute values to explore attribute coupling, but they only present a local picture in analyzing categorical data similarity. This is inadequate for deep analysis, and the computational complexity grows exponentially when the data scale increases. This paper proposes an efficient data-driven similarity learning approach that generates a coupled attribute similarity measure for nominal objects with attribute couplings to capture a global picture of attribute similarity. It involves the frequency-based intra-coupled similarity within an attribute and the inter-coupled similarity upon value co-occurrences between attributes, as well as their integration on the object level. In particular, four measures are designed for the inter-coupled similarity to calculate the similarity between two categorical values by considering their relationships with other attributes in terms of power set, universal set, joint set, and intersection set. The theoretical analysis reveals the equivalent accuracy and superior efficiency of the measure based on the intersection set, particularly for large-scale data sets. Intensive experiments of data structure and clustering algorithms incorporating the coupled dissimilarity metric achieve a significant performance improvement on state-of-the-art measures and algorithms on 13 UCI data sets, which is confirmed by the statistical analysis. The experiment results show that the proposed coupled attribute similarity is generic, and can effectively and efficiently capture the intrinsic and global interactions within and between attributes for especially large-scale categorical data sets. In addition, two new coupled categorical clustering algorithms, i.e., CROCK and CLIMBO are proposed, and they both outperform the original ones in terms of clustering quality on UCI data sets and bibliographic data. <s> BIB022 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Next basket recommendation becomes an increasing concern. Most conventional models explore either sequential transaction features or general interests of users. Further, some works treat users' general interests and sequential behaviors as two totally divided matters, and then combine them in some way for next basket recommendation. Moreover, the state-of-the-art models are based on the assumption of Markov Chains (MC), which only capture local sequential features between two adjacent baskets. In this work, we propose a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN). DREAM not only learns a dynamic representation of a user but also captures global sequential features among baskets. The dynamic representation of a specific user can reveal user's dynamic interests at different time, and the global sequential features reflect interactions of all baskets of the user over time. Experiment results on two public datasets indicate that DREAM is more effective than the state-of-the-art models for next basket recommendation. <s> BIB023 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> The cold start problem arises in various recommendation applications. In this paper, we propose a tensor factorization-based algorithm that exploits content features extracted from music audio to deal with the cold start problem for the emerging application next-song recommendation. Specifically, the new algorithm learns sequential behavior to predict the next song that a user would be interested in based on the last song the user just listened to. A unique characteristic of the algorithm is that it learns and updates the mapping between the audio feature space and the item latent space each time during the iterations of the factorization process. This way, the content features can be better exploited in forming the latent features for both users and items, leading to more effective solutions for cold-start recommendation. Evaluation on a large-scale music recommendation dataset shows that the recommendation result of the proposed algorithm exhibits not only higher accuracy but also better novelty and diversity, suggesting its applicability in helping a user explore new items in next-item recommendation. Our implementation is available at https://github.com/fearofchou/ALMM. <s> BIB024 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, CoFactor, which jointly decomposes the user-item interaction matrix and the item-item co-occurrence matrix with shared item latent factors. For each pair of items, the co-occurrence matrix encodes the number of users that have consumed both items. CoFactor is inspired by the recent success of word embedding models (e.g., word2vec) which can be interpreted as factorizing the word co-occurrence matrix. We show that this model significantly improves the performance over MF models on several datasets with little additional computational overhead. We provide qualitative results that explain how CoFactor improves the quality of the inferred factors and characterize the circumstances where it provides the most significant improvements. <s> BIB025 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively. <s> BIB026 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features. Existing pattern-based outlier detection methods are ineffective in handling such complex scenarios, as they misfit such data. CBRW estimates outlier scores of feature values by modelling feature value level couplings, which carry intrinsic data characteristics, via biased random walks to handle this complex data. The outlier scores of feature values can either measure the outlierness of an object or facilitate the existing methods as a feature weighting and selection indicator. Substantial experiments show that CBRW can not only detect outliers in complex data significantly better than the state-of-the-art methods, but also greatly improve the performance of existing methods on data sets with many noisy features. <s> BIB027 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems. Results have been promising, especially in the session-based setting where RNNs have been shown to outperform state-of-the-art models. In many of these experiments, the RNN could potentially improve the recommendations by utilizing information about the user's past sessions, in addition to its own interactions in the current session. A problem for session-based recommendation, is how to produce accurate recommendations at the start of a session, before the system has learned much about the user's current interests. We propose a novel approach that extends an RNN recommender to be able to process the user's recent sessions, in order to improve recommendations. This is done by using a second RNN to learn from recent sessions, and predict the user's interest in the current session. By feeding this information to the original RNN, it is able to improve its recommendations. Our experiments on two different datasets show that the proposed approach can significantly improve recommendations throughout the sessions, compared to a single RNN working only on the current session. The proposed model especially improves recommendations at the start of sessions, and is therefore able to deal with the cold start problem within sessions. <s> BIB028 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs. <s> BIB029 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> To predict customer’s next choice in the context of what he/she has bought in a session is interesting and critical in the transaction domain especially for online shopping. Precise prediction leads to high quality recommendations and thus high benefit. Such kind of recommendation is usually formalized as transaction-based recommender systems (TBRS). Existing TBRS either tend to recommend popular items while ignore infrequent and newly-released ones (e.g., pattern-based RS) or assume a rigid order between items within a transaction (e.g., Markov Chain-based RS) which does not satisfy real-world cases in most time. In this paper, we propose a neural network-based comprehensive transaction embedding model (NTEM) which can effectively perceive the next choice in a transaction context. Specifically, we learn these comprehensive embeddings of both items and their features from relaxed ordered transactions. The relevance between items revealed by the transactions is encoded into such embeddings. With rich information embedded, such embeddings are powerful to predict the next choices given those already bought items. NTEM is a shallow wide-in-wide-out network, which is more efficient than deep networks considering large numbers of items and transactions. Experimental results on real-world datasets show that NTEM outperforms three typical TBRS models FPMC, PRME and GRU4Rec in terms of recommendation accuracy and novelty. Our implementation is available at https://github.com/shoujin88/NTEM-model. <s> BIB030 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Many Recommender Systems (RSs) rely on user preference data in the form of ratings or likes for items. Previous research has shown that item comparisons can also be effectively used to model user preferences and build RS. However, users often express their preferences by referring to specific features of the items. For instance, a user may like Italian movies more than Indian ones or like action-thriller movies. In this paper, we map such preferences over features to comparisons between items. For instance, when a user's favorite feature is `action', we then assume that `action' movies are preferred to some of the movies that are not `action'. In this work we effectively incorporate these feature based comparisons in a RS and show that such preferences can be effectively combined along with other item comparisons. Moreover, we also study the usefulness of the available features. <s> BIB031 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> This paper provides an overview of the approach we adopted as team Lunatic Goats for the ACM RecSys Challenge 2017 [7]. The competition, organized by XING.com, focuses on a cold start job recommendation scenario. The goal was to design and tune a recommendation system able to predict past users' interactions, for the offline stage, and to provide recommendations pushed every day to real users through the XING portal, for the online stage. Our strategy, which saw models coming from different techniques combined in a multi-layer ensemble, granted us the first place in the offline part and the qualification as second best team in the final leaderboard. All our algorithms mainly resort to content-based approaches, that, thanks to its ability to provide good recommendations even for cold-start items allowed us, quite unexpectedly, to achieve good results in terms of prediction quality and computational time. <s> BIB032 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Cold start remains a prominent problem in recommender systems. While rich content information is often available for both users and items few existing models can fully exploit it for personalization. Slow progress in this area can be partially attributed to the lack of publicly available benchmarks to validate and compare models. This year's ACM Recommender Systems Challenge' 17 aimed to address this gap by providing a standardized framework to benchmark cold start models. The challenge organizer XING released a large scaled data collection of user-job interactions from their career oriented social network. Unlike other competitions, here the participating teams were evaluated in two phases -- offline and online. Models were first evaluated on the held-out offline test set. Top models were then A/B tested in the online phase where new target users and items were released daily and recommendations were pushed into XING's live production system. In this paper we present our approach to this challenge, we used a combination of content and neighbor-based models winning both offline and online phases. Our model produced the most consistent online performance wining four of the five online weeks, and showed excellent generalization in the live A/B setting. <s> BIB033 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Recurrent neural networks have recently been successfully applied to the session-based recommendation problem, and is part of a growing interest for collaborative filtering based on sequence prediction. This new approach to recommendations reveals an aspect that was previously overlooked: the difference between short-term and long-term recommendations. In this work we characterize the full short-term/long-term profile of many collaborative filtering methods, and we show how recurrent neural networks can be steered towards better short or long-term predictions. We also show that RNNs are not only adapted to session-based collaborative filtering, but are perfectly suited for collaborative filtering on dense datasets where it outperforms traditional item recommendation algorithms. <s> BIB034 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks. For the problem of making session-based recommendations, i.e., for recommending the next item in an anonymous session, Hidasi et al.~recently investigated the application of recurrent neural networks with Gated Recurrent Units (GRU4REC). Assessing the true effectiveness of such novel approaches based only on what is reported in the literature is however difficult when no standard evaluation protocols are applied and when the strength of the baselines used in the performance comparison is not clear. In this work we show based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets. Neighborhood sampling and efficient in-memory data structures ensure the scalability of the kNN method. The best results in the end were often achieved when we combine the kNN approach with GRU4REC, which shows that RNNs can leverage sequential signals in the data that cannot be detected by the co-occurrence-based kNN method. <s> BIB035 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task. <s> BIB036 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously. <s> BIB037 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data. Existing outlier detection methods work on a full data space or feature subspaces that are identified independently from subsequent outlier scoring. As a result, they are significantly challenged by overwhelming irrelevant features in high-dimensional data due to the noise brought by the irrelevant features and its huge search space. In contrast, SelectVC works on a clean and condensed data space spanned by selective value couplings by jointly optimizing outlying value selection and value outlierness scoring. Its instance POP defines a value outlierness scoring function by modeling a partial outlierness propagation process to capture the selective value couplings. POP further defines a top-k outlying value selection method to ensure its scalability to the huge search space. We show that POP (i) significantly outperforms five state-of-the-art full space- or subspace-based outlier detectors and their combinations with three feature selection methods on 12 real-world high-dimensional data sets with different levels of irrelevant features; and (ii) obtains good scalability, stable performance w.r.t. k, and fast convergence rate. <s> BIB038 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Financial variables such as asset returns in the massive market contain various hierarchical and horizontal relationships that form complicated dependence structures. Modeling these structures is challenging due to the stylized facts of market data. Many research works in recent decades showed that copula is an effective method to describe relations among variables. Vine structures were introduced to represent the decomposition of multivariate copula functions. However, the model construction of vine structures is still a tough problem owing to the geometrical data, conditional independent assumptions and the stylized facts. In this paper, we introduce a new bottom-to-up method to construct regular vine structures and applies the model to 12 currencies over 16 years as a case study to analyze the asymmetric and fat tail features. The out-of-sample performance of our model is evaluated by Value at Risk, a widely used industrial benchmark. The experimental results show that our model and its intrinsic design significantly outperform industry baselines, and provide financially interpretable knowledge and profound insights into the dependence structures of multi-variables with complex dependencies and characteristics. <s> BIB039 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Recurrent Neural Networks have been successful in learning meaningful representations from sequence data, such as text and speech. However, recurrent neural networks attempt to model only the overall structure of each sequence independently, which is unsuitable for recommendations. In recommendation system, an optimal model should not only capture the global structure, but also the localized relationships. This poses a great challenge in the application of recurrent neural networks to the sequence prediction problem. To tackle this challenge, we incorporate the neighbor sequences into recurrent neural networks to help detect local relationships. Thus we propose a K -plet R ecurrent Neural Network (Kr Network for short) to accommodate multiple sequences jointly, and then introduce two ways to model their interactions between sequences. Experimental results on benchmark datasets show that our proposed architecture Kr Network outperforms state-of-the-art baseline methods in terms of generalization, short-term and long term prediction accuracy. <s> BIB040 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or itemitem interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones. <s> BIB041 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The improved performance of these losses over alternatives, along with further tricks and refinements described in this work, allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly. We further demonstrate the performance gain of the RNN over baselines in an online A/B test. <s> BIB042 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Predicting users' actions based on anonymous sessions is a challenging problem in web-based behavioral modeling research, mainly due to the uncertainty of user behavior and the limited information. Recent advances in recurrent neural networks have led to promising approaches to solving this problem, with long short-term memory model proving effective in capturing users' general interests from previous clicks. However, none of the existing approaches explicitly take the effects of users' current actions on their next moves into account. In this study, we argue that a long-term memory model may be insufficient for modeling long sessions that usually contain user interests drift caused by unintended clicks. A novel short-term attention/memory priority model is proposed as a remedy, which is capable of capturing users' general interests from the long-term memory of a session context, whilst taking into account users' current interests from the short-term memory of the last-clicks. The validity and efficacy of the proposed attention mechanism is extensively evaluated on three benchmark data sets from the RecSys Challenge 2015 and CIKM Cup 2016. The numerical results show that our model achieves state-of-the-art performance in all the tests. <s> BIB043 </s> A Survey on Session-based Recommender Systems <s> How to Recommend: A Categorization of recommendation approaches. <s> Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability. <s> BIB044
A major driving force in SBRS is couplings, or more specifically dependency BIB044 BIB010 BIB039 . This is because of the particular session-based recommendation setting: given a session context, recommendations are made by finding those item(s) or events that are most dependent on the context as the next choice. Naturally, the answer to the critical question "how to recommend" is to model the dependency embedded in the session data. Essentially, there is quite complex dependency within each level (cf. Fig. 2 ) or between different levels in the session data. Correspondingly, the approaches to session-based recommendations generally model the complex dependency. Based on the hierarchical structure of session data illustrated in Section 3.3, the relevant approaches can be divided into five branches. Each branch corresponds to the modelling of dependency on one level. (a) Item-level Dependency Modeling. The item-level dependency modeling refers to the modelling of inter-item or inter-event dependency within a session for next item(s) or next event recommendations. This is also called intra-session dependency modelling. Currently, most existing work in SBRS falls into this branch. There are several typical issues in the item-level dependency modelling which could make a huge difference on the quality of dependency modelling and the performance of recommendations. Next-basket recommendations Some progress has been made to model inter-session dependency with typical approaches including factorization machines and neural models. BIB005 BIB018 BIB019 BIB023 Next-event/action recommendations Some progress has been made to model sequential or non-sequential inter-event dependency with typical approaches including factorization machines, latent Markov models (also called metric embeddings), and neural models.  BIB014 BIB024 BIB020 BIB015 BIB040 BIB016 -Ordered vs. unordered items. The real-world session data usually falls into two types -ordered data and unordered ones, according to whether there is order over the items in a session or not  . For instance, in the sequence data of medical treatment or in the gene expression data, the order of treatment activities or genes are quite strict and cannot be destroyed. While in the grocery shopping-basket data, the order over items within a shopping basket may not make much sense since many customers may just randomly pick up and put items into the basket. In addition, usually the original order over which the items are put into the cart cannot be kept well in the transaction recording data in real-world cases. To this end, some approaches working on the item-level dependency for session-based recommendations assume a strict order over items within a session, and they mainly rely on the sequential dependency over items. A naive approach is the sequential pattern mining-based RS, which explicitly mine frequent sequential patterns to guide the recommendations. The details will be given in Sections 5.2 and 6.2. To capture more implicit sequential dependency over items within sessions and keep the original information as much as possible, the Markov Chain-based approaches and the neural model-based approaches, especially the RNN-based approaches, are proposed. The Markov Chain models and RNN have the natural advantages to capture the comprehensive sequential dependency due to their particular model designs for the time series data, more details will be given in Sections 5.2 and 7. Other approaches try to relax or avoid the ordering assumption to make them more flexible and practical. In such a case, these approaches do not have a ordering assumption over items. The pattern/rule-based approaches are a simple and straightforward one. They explicitly discover the frequent patterns based on item co-occurrences to guide recommendations and will be detailed in Sections 5.2 and 6.1. To keep both frequent and non-frequent items in the datasets and to capture more comprehensive implicit dependency over items, more advanced approaches are employed. They include the factorization machine-based approaches, the shallow neural networks (also called as embedding)-based ones, the naive deep neural network (DNN)-based ones, and the convolutional neural network (CNN)-based ones. They will be detailed in Sections 5.2 and 7. -First-order dependency vs. higher-order dependency. Some dependency in the session data is first-order while others may be higher-order  . Accordingly, some approaches are built on the first-order dependency assumption and only capture the first-order dependency over items; in other words, they predict the next item(s) by only considering the immediately prior item. Typical approaches belonging to this category include the first-order Markov chain-based approaches and the factorization-based approaches. Details on these approaches will be given in Sections 7.1 and 7.2. However, most session data not only involves the first-order dependency but also the higher-order dependency. In this case, more advanced approaches such as neural model-based approaches are necessary to capture higher-order dependency for more sound recommendations. The neural model-based approaches, including both shallow and deep neural networks, can comprehensively capture both first-order and higher-order dependencies by utilizing the complex network structures. Details on such kind of approaches are given in Section 7.3. (b) Session-level Dependency Modelling. The session-level dependency modeling refers to the modelling of intersession dependency for session-based recommendations BIB028 . Since a session is built on items, the session-level dependency modeling is often accompanied by the item-level dependency modeling. From the learning task perspective, the session-level dependency modelling approaches are divided into next-item(s) recommendations and next-basket recommendations. In next-item(s) recommendations, the session-level dependency modelling is employed to incorporate the inter-session dependency and thus take the effect of previous sessions into account BIB029 BIB041 . As a result, this can enhance the prior information for next-item(s) recommendations compared to those ones only modelling the intra-session dependency. In next-basket recommendations, the session-level dependency modelling is necessary to capture the inter-session dependency to recommend what would be bought in the next basket by finding the connections between the candidate items and the items in recent baskets BIB023 . According to how to model the inter-session dependency, the session-level dependency modelling approaches can be mainly divided into two classes: item dependency modelling and Collective dependency modelling. -Item dependency modelling. The item dependency modelling models the item-to-item transitions from one basket to another separately. In other words, it models the inter-item dependency where the items are from different sessions. This kind of approaches first explore the transitions of items between sessions, and then predict the items that may occur in the next basket based on those items that have been put in the current baskets. Factorization machine has been employed to generate next basket recommendations by modelling the item-to-item dependency BIB005 . -Collective dependency modelling. Different from the above mentioned item dependency modelling, the collective dependency modelling models the inter-session dependency by taking each session as a whole. A representation of the whole session can be learned by a hierarchical neural model which first learns the item representation and then integrates the representation of each item in the session. During the training of the model, the next session is usually used as the output to guide the representation learning of recent sessions, therefore the learned representations can actually reflect the dependency between historical sessions and next one BIB029 BIB018 BIB019 BIB023 . (c) Feature-level Dependency Modelling. The feature-level dependency modeling refers to the approaches of modelling the inter-feature dependency BIB021 BIB022 and the feature-to-item dependency BIB030 for session-based recommendations. The inter-feature dependency means one feature of an item may somehow influence its another features, for instance, apples produced in different countries usually have different prices. The featureto-item dependency indicates the occurrences of items in a session are usually affected by their features, for example, the items belonging to the same category (e.g., food) but with complementary functions (e.g., eating and drinking) are often bought together and thus occur in the same session (i.e., transaction). The involvement of item features enables us to understand the item intrinsic characteristics from a low level perspective. As a result, the feature-level dependency modeling can often greatly benefit the recommendations of cold-start items which have quite limited observed data on the item level and the session level BIB031 . Much progress has been made on modelling the feature-level dependency for cold-start item recommendations in conventional RS including content-based ones BIB032 BIB033 and collaborative filtering ones BIB006 BIB008 . However, the feature-level dependency modelling has been less studied in SBRS and is at its early research stage. Only few researchers start to address the cold start issues by incorporating the content features into a neural model or a factorization model for next-item BIB030 or next-song BIB024 recommendations. Much progress has been made to generally model explicit inter-item dependency within sessions with various approaches including pattern-based approaches BIB004 BIB002 BIB001 and factorization machines BIB025 to particularly capture implicit inter-item relations with approaches like implicit rule learning BIB044 , to particularly model the sequential inter-item dependency with approaches including sequential pattern-based models BIB011 , Markov chain models BIB003 BIB012 , RNN models  BIB034 BIB042  BIB035 BIB040 BIB029 BIB036 BIB026 , to particularly address the item heterogeneity issue with the attention mechanism BIB037 BIB043  . Item imbalance issue (e.g., how to handle tailed items), noise and irrelevant items Session-level dependency modelling Much progress has been made in areas such as modelling general inter-session transitions with factorization machines BIB005 , neural representation learning methods BIB018 BIB019 BIB009 and the feature value-to-feature-to-item interactions for session-based recommendations. The domain-level dependency modeling captures the dependency between different domains to help with the session-based recommendations in one domain or in both domains, which will be discussed in Section 8.6. To the best of our knowledge, there is no work exploring the feature value-level dependency or domain-level dependency in SBRS. Some work has been done to explore the feature value-level dependency for other tasks including object representation learning BIB009 and outlier detection BIB027 BIB038 . The significance of domain-level dependency has been demonstrated by the great success of transfer learning  . Some work has focused on modelling the domain-level dependency with the aim of borrowing some prior information from source domains to help with the recommendations in the target domain BIB013 . However, most of such work falls into cross-domain collaborative filtering, which builds a bridge between the source domain and the target domain to address the data sparsity issue in collaborative filtering RS BIB017 BIB007 . In SBRS, there is no existing work focusing on domain-level dependency modeling to the best of our knowledge. However, based on our observations, this would be a very promising direction in the future, as will be discussed in Section 8.