A Survey on Session-based Recommender Systems <s> Session-based Recommendations with Cross-domain Information <s> Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations with Cross-domain Information <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> BIB002 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations with Cross-domain Information <s> Cross-domain collaborative filtering (CDCF), which aims to leverage data from multiple domains to relieve the data sparsity issue, is becoming an emerging research topic in recent years. However, current CDCF methods that mainly consider user and item factors but largely neglect the heterogeneity of domains may lead to improper knowledge transfer issues. To address this problem, we propose a novel CDCF model, the Bilinear Multilevel Analysis (BLMA), which seamlessly introduces multilevel analysis theory to the most successful collaborative filtering method, matrix factorization (MF). Specifically, we employ BLMA to more efficiently address the determinants of ratings from a hierarchical view by jointly considering domain, community, and user effects so as to overcome the issues caused by traditional MF approaches. Moreover, a parallel Gibbs sampler is provided to learn these effects. Finally, experiments conducted on a realworld dataset demonstrate the superiority of the BLMA over other state-of-the-art methods. <s> BIB003 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations with Cross-domain Information <s> A major challenge for collaborative filtering (CF) techniques in recommender systems is the data sparsity that is caused by missing and noisy ratings. This problem is even more serious for CF domains where the ratings are expressed numerically, e.g. as 5-star grades. We assume the 5-star ratings are unordered bins instead of ordinal relative preferences. We observe that, while we may lack the information in numerical ratings, we sometimes have additional auxiliary data in the form of binary ratings. This is especially true given that users can easily express themselves with their preferences expressed as likes or dislikes for items. In this paper, we explore how to use these binary auxiliary preference data to help reduce the impact of data sparsity for CF domains expressed in numerical ratings. We solve this problem by transferring the rating knowledge from some auxiliary data source in binary form (that is, likes or dislikes), to a target numerical rating matrix. In particular, our solution is to model both the numerical ratings and ratings expressed as like or dislike in a principled way. We present a novel framework of Transfer by Collective Factorization (TCF), in which we construct a shared latent space collectively and learn the data-dependent effect separately. A major advantage of the TCF approach over the previous bilinear method of collective matrix factorization is that we are able to capture the data-dependent effect when sharing the data-independent knowledge. This allows us to increase the overall quality of knowledge transfer. We present extensive experimental results to demonstrate the effectiveness of TCF at various sparsity levels, and show improvements of our approach as compared to several state-of-the-art methods. <s> BIB004 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations with Cross-domain Information <s> Factorization machines offer an advantage over other existing collaborative filtering approaches to recommendation. They make it possible to work with any auxiliary information that can be encoded as a real-valued feature vector as a supplement to the information in the user-item matrix. We build on the assumption that different patterns characterize the way that users interact with i.e., rate or download items of a certain type e.g., movies or books. We view interactions with a specific type of item as constituting a particular domain and allow interaction information from an auxiliary domain to inform recommendation in a target domain. Our proposed approach is tested on a data set from Amazon and compared with a state-of-the-art approach that has been proposed for Cross-Domain Collaborative Filtering. Experimental results demonstrate that our approach, which has a lower computational complexity, is able to achieve performance improvements. <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations with Cross-domain Information <s> Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie/TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49% enhancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain. <s> BIB006 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations with Cross-domain Information <s> Abstract Complex applications such as big data analytics involve different forms of coupling relationships that reflect interactions between factors related to technical, business (domain-specific) and environmental (including socio-cultural and economic) aspects. There are diverse forms of couplings embedded in poor-structured and ill-structured data. Such couplings are ubiquitous, implicit and/or explicit, objective and/or subjective, heterogeneous and/or homogeneous, presenting complexities to existing learning systems in statistics, mathematics and computer sciences, such as typical dependency, association and correlation relationships. Modeling and learning such couplings thus is fundamental but challenging. This paper discusses the concept of coupling learning, focusing on the involvement of coupling relationships in learning systems. Coupling learning has great potential for building a deep understanding of the essence of business problems and handling challenges that have not been addressed well by existing learning theories and tools. This argument is verified by several case studies on coupling learning, including handling coupling in recommender systems, incorporating couplings into coupled clustering, coupling document clustering, coupled recommender algorithms and coupled behavior analysis for groups. <s> BIB007
Siдni f icance. Cross-domain means another domain that is different from but relevant to the target domain in which the recommendations are made BIB003 . Usually, a user's purchased items involve multiple product categories (domains) rather than one domain to meet her/his compound demand. In addition, the choices on items from different domains may not be independent from each other. For instance, a user may have seen a movie "Titanic" first and enjoyed it greatly and then he may listen to the movie's theme song "My heart will go on" and likes the singer "Céline" very much, as a result, he may listen to other songs such as "I'm Your Angel" by "Céline". In another case, young girls who like the protagonist "Rose" in Titanic are likely to buy the same dress as Rose's. Such examples show that the items from different domains may not only be dependent but can even form a sequential session, such as {Titanic, My heart will go on, Céline, I'm Your Angel} or {Titanic, Rose, Rose's style dress}. The recommendations based on such type of sessions are interesting but quite challenging. On one hand, such recommendations not only cover more aspects of our daily living activities but also provide a solution to the data sparsity issue when only one domain is considered. On the other hand, it is hard to collect a user's consumed products or services from various domains together, and the relations between items from different domains are much more complex than that from one domain. Open issues. According to whether the products or services from different domains can form a tight session or not, there are two open issues to be further explored. -How to borrow knowledge from other domains to help with the session-based recommendations in the target domain? When no sessions can be built over products or services from different domains, the way to make use of other domains is a target-auxiliary framework. To be specific, it takes the target domain where the recommendations are made as the main information source while taking other domains as a supplementary one to leverage the recommendation performance. An intuitive choice is transfer learning BIB006  which transfers knowledge from source domains to help with the tasks in the target one. Although transfer learning has been well applied in other RS like collaborative filtering BIB001 BIB005 BIB002 BIB004 , it is rarely explored in SBRS. -How to make session-based recommendations from multiple domains? This case happens when a session can be built on elements from different domains, like the aforementioned example {Titanic, My heart will go on, Céline, I'm Your Angel}. Different from the aforementioned target-auxiliary framework, this case treats all elements from different domains equally and each domain can serve as the target domain. It is much more interesting yet challenging than the above case. It can incorporate nearly all of a user's daily activities into a unified RS framework. However, the challenges come from two aspects: session-building and model development. Different from session-based recommendations in one domain, where the natural sessions are usually already there, almost no obvious session on cross-domains are available. Therefore, how to build a reasonable session from multiple heterogeneous data sources is the first challenge. Once the cross-domain sessions are ready, how to develop a model to effectively capture the complex and heterogeneous coupling relationships BIB007 between elements from different domains is another challenge.