A Survey on Session-based Recommender Systems <s> Model-based 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> BIB001 </s> A Survey on Session-based Recommender Systems <s> Model-based 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> BIB002 </s> A Survey on Session-based Recommender Systems <s> Model-based 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> BIB003 </s> A Survey on Session-based Recommender Systems <s> Model-based 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> BIB004 </s> A Survey on Session-based Recommender Systems <s> Model-based Approaches <s> We present a method of generating item recommendations by learning item feature vector embeddings. Our work is analogous to approaches like Word2Vec or Glove used to generate a good vector representation of words in a natural language corpus. We treat the items that a user interacted with as analogous to words and the string of items interacted with in a session as sentences. Our embedding generates semantically related clusters and the item vectors generated can be used to compute item similarity which can be used to drive product recommendations. Our method also allows us to use the feature vectors in other machine learning systems. We validate our method on the MovieLens dataset. <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> Model-based Approaches <s> Deep learning has become increasingly popular in both academic and industrial areas in the past years. Various domains including pattern recognition, computer vision, and natural language processing have witnessed the great power of deep networks. However, current studies on deep learning mainly focus on data sets with balanced class labels, while its performance on imbalanced data is not well examined. Imbalanced data sets exist widely in real world and they have been providing great challenges for classification tasks. In this paper, we focus on the problem of classification using deep network on imbalanced data sets. Specifically, a novel loss function called mean false error together with its improved version mean squared false error are proposed for the training of deep networks on imbalanced data sets. The proposed method can effectively capture classification errors from both majority class and minority class equally. Experiments and comparisons demonstrate the superiority of the proposed approach compared with conventional methods in classifying imbalanced data sets on deep neural networks. <s> BIB006 </s> A Survey on Session-based Recommender Systems <s> Model-based Approaches <s> Recent methods for learning vector space representations of words, word embedding, such as GloVe and Word2Vec have succeeded in capturing fine-grained semantic and syntactic regularities. We analyzed the effectiveness of these methods for e-commerce recommender systems by transferring the sequence of items generated by users' browsing journey in an e-commerce website into a sentence of words. We examined the prediction of fine-grained item similarity (such as item most similar to iPhone 6 64GB smart phone) and item analogy (such as iPhone 5 is to iPhone 6 as Samsung S5 is to Samsung S6) using real life users' browsing history of an online European department store. Our results reveal that such methods outperform related models such as singular value decomposition (SVD) with respect to item similarity and analogy tasks across different product categories. Furthermore, these methods produce a highly condensed item vector space representation, item embedding, with behavioral meaning sub-structure. These vectors can be used as features in a variety of recommender system applications. In particular, we used these vectors as features in a neural network based models for anonymous user recommendation based on session's first few clicks. It is found that recurrent neural network that preserves the order of user's clicks outperforms standard neural network, item-to-item similarity and SVD (recall@10 value of 42% based on first three clicks) for this task. <s> BIB007
. Different from the model-free RS, model-based RS are usually built on strict assumptions like ordering over items and complex models such as Markov chain models. Existing model-based approaches can be mainly categorized into three classes according to the models they involved: Markov Chainbased approaches, factorization-based approaches, and neural model-based approaches. (a) Markov Chain-based Approaches. Markov Chain-based RS models the first-order (sometimes higher order) dependency over a sequence of items by using transitional probabilities and then generates the recommendations of the following items by utilizing such dependency BIB001 BIB002 BIB003 . Different from sequential pattern-based approaches which are easy to filter out those infrequent items and patterns and thus lead to information loss, Markov Chain-based RS take all items into consideration and thus decrease the information loss greatly. (b) Factorization-based Approaches. These approaches first factorize the item co-occurrence matrix or the item-to-item transitional matrix into a latent representation vector of each item and then predict the following items by using these latent representations BIB004 BIB002 . Such approaches should be distinguished from the commonly used factorization machine (like matrix factorization) in collaborative filtering-based RS, which usually factorize the user-item interaction matrix (like rating matrix) into latent factors of users and items respectively   . (c) Neural Model-based Approaches. Neural model-based approaches take advantage of the neural network to learn the complex relationships and interactions over items within or between sessions and then generate recommendations based on such interactions. Based on the model structure, neural model-based approaches can be divided into shallow neural model-based approaches, which sometimes are also called embedding models or representation learning models BIB007  BIB005  Generally speaking, model-free approaches are simple, straightforward and easy to implement. Since both pattern/rule-based and sequential pattern-based approaches are frequency-based only, they are easy to filter out infrequent but significant items or patterns. As a result, model-free approaches are suitable for the relation discovery between frequent items in simple datasets. However, they may easily fail to model complex dependency in complex datasets for session-based recommendations. In contrast, model-based approaches are much more complicated but powerful in complex datasets. On one hand, they do not explicitly filter out items or patterns from the datasets, but retain the information to the maximum extent. On the other hand, thanks to the complex working mechanisms such as deep neural networks BIB006 , these model-based approaches are able to model the complex and implicit relations hidden in the data, which results in more solid session-based recommendations. More detailed comparisons between different specific approaches are provided in Table 5 . 