A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Abstract We consider specific additive decompositions d = d 1 + … + d n of metrics, defined on a finite set X (where a metric may give distance zero to pairs of distinct points). The simplest building stones are the slit metrics , associated to splits (i.e., bipartitions) of the given set X . While an additive decomposition of a Hamming metric into split metrics is in no way unique, we achieve uniqueness by restricting ourselves to coherent decompositions, that is, decompositions d = d 1 + … + d n such that for every map f : X → R with f ( x ) + f ( y ) ⩾ d ( x , y ) for all x , y ϵ X there exist maps f 1 , …, f n : X → R with f = f 1 + … + f n and f i ( x ) + f i ( y ) ⩾ d i ( x , y ) for all i = 1,…, n and all x , y ϵ X . These coherent decompositions are closely related to a geometric decomposition of the injective hull of the given metric. A metric with a coherent decomposition into a (weighted) sum of split metrics will be called totally split-decomposable . Tree metrics (and more generally, the sum of two tree metrics) are particular instances of totally split-decomposable metrics. Our main result confirms that every metric admits a coherent decomposition into a totally split-decomposable metric and a split-prime residue, where all the split summands and hence the decomposition can be determined in polynomial time, and that a family of splits can occur this way if and only if it does not induce on any four-point subset all three splits with block size two. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Real-life recommender systems often face the daunting task of providing recommendations based only on the clicks of a user session. Methods that rely on user profiles -- such as matrix factorization -- perform very poorly in this setting, thus item-to-item recommendations are used most of the time. However the items typically have rich feature representations such as pictures and text descriptions that can be used to model the sessions. Here we investigate how these features can be exploited in Recurrent Neural Network based session models using deep learning. We show that obvious approaches do not leverage these data sources. We thus introduce a number of parallel RNN (p-RNN) architectures to model sessions based on the clicks and the features (images and text) of the clicked items. We also propose alternative training strategies for p-RNNs that suit them better than standard training. We show that p-RNN architectures with proper training have significant performance improvements over feature-less session models while all session-based models outperform the item-to-item type baseline. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <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> BIB003 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <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> BIB004 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Preparing recommendations for unknown users or such that correctly respond to the short-term needs of a particular user is one of the fundamental problems for e-commerce. Most of the common Recommender Systems assume that user identification must be explicit. In this paper a Session-Aware Recommender System approach is presented where no straightforward user information is required. The recommendation process is based only on user activity within a single session, defined as a sequence of events. This information is incorporated in the recommendation process by explicit context modeling with factorization methods and a novel approach with Recurrent Neural Network (RNN). Compared to the session modeling approach, RNN directly models the dependency of user observed sequential behavior throughout its recurrent structure. The evaluation discusses the results based on sessions from real-life system with ephemeral items (identified only by the set of their attributes) for the task of top-n best recommendations. <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow. <s> BIB006 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Modeling temporal behavior in recommendation systems is an important and challenging problem. Its challenges come from the fact that temporal modeling increases the cost of parameter estimation and inference, while requiring large amount of data to reliably learn the model with the additional time dimensions. Therefore, it is often difficult to model temporal behavior in large-scale real-world recommendation systems. In this work, we propose a novel deep neural network based architecture that models the combination of long-term static and short-term temporal user preferences to improve the recommendation performance. To train the model efficiently for large-scale applications, we propose a novel pre-train method to reduce the number of free parameters significantly. The resulted model is applied to a real-world data set from a commercial News recommendation system. We compare to a set of established baselines and the experimental results show that our method outperforms the state-of-the-art significantly. <s> BIB007 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <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> BIB008 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <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> BIB009 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network. <s> BIB010 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques. <s> BIB011 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods. <s> BIB012 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <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> BIB013 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> The ability to predict future user activity is invaluable when it comes to content recommendation and personalization. For instance, knowing when users will return to an online music service and what they will listen to increases user satisfaction and therefore user retention. We present a model based on Long-Short Term Memory to estimate when a user will return to a site and what their future listening behavior will be. In doing so, we aim to solve the problem of Just-In-Time recommendation, that is, to recommend the right items at the right time. We use tools from survival analysis for return time prediction and exponential families for future activity analysis. We show that the resulting multitask problem can be solved accurately, when applied to two real-world datasets. <s> BIB014 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function. <s> BIB015 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Most of the existing recommender systems assume that user's visiting history can be constantly recorded. However, in recent online services, the user identification may be usually unknown and only limited online user behaviors can be used. It is of great importance to model the temporal online user behaviors and conduct recommendation for the anonymous users. In this paper, we propose a list-wise deep neural network based architecture to model the limited user behaviors within each session. To train the model efficiently, we first design a session embedding method to pre-train a session representation, which incorporates different kinds of user search behaviors such as clicks and views. Based on the learnt session representation, we further propose a list-wise ranking model to generate the recommendation result for each anonymous user session. We conduct quantitative experiments on a recently published dataset from an e-commerce company. The evaluation results validate the effectiveness of the proposed method, which can outperform the state-of-the-art. <s> BIB016 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the capability of recommender systems to help users discover items they presumably do not know yet and which match their long-term preference profiles. In reality, however, recommendations can be helpful for customers also for other reasons, for example, when they remind them of items they were recently interested in or when they point site visitors to items that are currently discounted. In this work, we first adopt a systematic statistical approach to analyze what makes recommendations effective in practice and then propose ways of operationalizing these insights into novel recommendation algorithms. Our data analysis is based on log data of a large e-commerce site. It shows that various factors should be considered in parallel when selecting items for recommendation, including their match with the customer's shopping interests in the previous sessions, the general popularity of the items in the last few days, as well as information about discounts. Based on these analyses we propose a novel algorithm that combines a neighborhood-based scheme with a deep neural network to predict the relevance of items for a given shopping session. <s> BIB017 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> In many real-life recommendation settings, user profiles and past activities are not available. The recommender system should make predictions based on session data, e.g. session clicks and descriptions of clicked items. Conventional recommendation approaches, which rely on past user-item interaction data, cannot deliver accurate results in these situations. In this paper, we describe a method that combines session clicks and content features such as item descriptions and item categories to generate recommendations. To model these data, which are usually of different types and nature, we use 3-dimensional convolutional neural networks with character-level encoding of all input data. While 3D architectures provide a natural way to capture spatio-temporal patterns, character-level networks allow modeling different data types using their raw textual representation, thus reducing feature engineering effort. We applied the proposed method to predict add-to-cart events in e-commerce websites, which is more difficult then predicting next clicks. On two real datasets, our method outperformed several baselines and a state-of-the-art method based on recurrent neural networks. <s> BIB018 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> A fundamental role of news websites is to recommend articles that are interesting to read. The key challenge of news recommendation is to recommend newly published articles. Unlike other domains, outdated items are considered to be irrelevant in the news recommendation task. Another challenge is that the recommendation candidates are not seen in the training phase. In this paper, we introduce deep neural network models to overcome these challenges. we propose a modified session-based Recurrent Neural Network (RNN) model tailored to news recommendation as well as a history-based RNN model that spans the whole user's past histories. Finally, we propose a Convolutional Neural Network (CNN) model to capture user preferences and to personalize recommendation results. Experimental results on real-world news dataset shows that our model outperforms competitive baselines. <s> BIB019 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <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> BIB020 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> The success of recommender systems often depends on their ability to understand and make use of the context of the recommendation request. Significant research has focused on how time, location, interfaces, and a plethora of other contextual features affect recommendations. However, in using deep neural networks for recommender systems, researchers often ignore these contexts or incorporate them as ordinary features in the model. In this paper, we study how to effectively treat contextual data in neural recommender systems. We begin with an empirical analysis of the conventional approach to context as features in feed-forward recommenders and demonstrate that this approach is inefficient in capturing common feature crosses. We apply this insight to design a state-of-the-art RNN recommender system. We first describe our RNN-based recommender system in use at YouTube. Next, we offer "Latent Cross," an easy-to-use technique to incorporate contextual data in the RNN by embedding the context feature first and then performing an element-wise product of the context embedding with model's hidden states. We demonstrate the improvement in performance by using this Latent Cross technique in multiple experimental settings. <s> BIB021 </s> A Survey on Session-based Recommender Systems <s> Deep Neural Models. <s> Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a solution to address this requirement. The idea is to embed a sequence of recent items into an `image' in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public datasets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics. <s> BIB022
It is generally believed that the deep neural models for SBRS started in 2016 when a gated recurrent unit (GRU)-based RNN model (GRU4Rec) was designed for SBRS in  . Following this work, a series of deep neural models have been proposed for SBRS, like BIB020 BIB002 BIB008 BIB003 . Out of these, RNN-based models dominated this area due to its intrinsic advantages for modeling sequential dependency, since a majority of session data is assumed to be sequentially dependent. DNN-based models are usually applied to learn an optimized combination of different representations for the following recommendations. CNN-based models are often adopted to extract more informative local features from sessions and other contents for better recommendations. Next, we introduce the representative models for SBRS under different network architectures respectively. (a) RNN-based Models. We first introduce the basic GRU4Rec model  , then present some improved versions and lastly show some variants of RNN-based SBRS. Given a session set S as defined in 2.2, where each session s ∈ S corresponds to a sequence of items bought consecutively in a transaction. GRU4Rec models each session as a sequence, in which it tries to predict a probability distribution over the next elements of the sequence conditioned on the current hidden state. A GRU is an advanced RNN unit aiming at handling gradient vanishing problems by learning when and by how much to update the hidden state  . Specifically, its hidden state h t is updated as follows: BIB001 where the update gate z t and the candidate hidden stateĥ t are computed by the following equations respectively. z t actually decides how much the unit updates its hidden state from the last state. where σ is the logistic sigmoid function and the reset gate r t is given by: Each GRU unit represents one hidden state and a GRU layer consisting of a sequence of connected GRU units forms a hidden layer of a RNN. In this case, a l-length sequence can be modeled by a hidden layer with l GRU units. The input x t is the embedding of item i t in a session s. Therefore, given the past items in s, GRU4Rec is built to model the intra-session dependency over sequential items and thus predict the probability distribution on the next item. To improve GRU4Rec, Tan etc. BIB003 adopted data augmentation via sequence preprocessing and embedding dropout to enhance training and reduce overfitting respectively. In addition, a model pre-training approach is proposed to consider the possible temporal shift in the distribution of input data. Quadrana etc. BIB008 further improved GRU4Rec by proposing a hierarchical RNN model to incorporate cross-session information for recommendations. Specifically, a two-level GRU-based RNN is designed: the session-level GRU models the sequence of items purchased by a user within a session and generates recommendations for next item(s), while the userlevel GRU models the cross-session information transfer and then provides personalized information to the session-level GRU by initializing its hidden state. To this end, both the intra-and inter-session dependencies are captured to generate more reliable next item(s) recommendations. Another quite similar work is Inter-Intra RNN (II-RNN) proposed in BIB009 . In BIB010 , the authors designed a unique user-based GRU model which incorporates user characteristic to generalize personalized next item recommendations. Except for the GRU-like RNN-based RS mentioned above, there are many other variants for RNN-based SBRS. For example, the Dynamic REcurrent bAsket Model (DREAM) BIB004 learns a dynamic representation of a user by utilizing the hidden state h u t of user u at time step t as the representation of u at time t while the input for each state is the embedding of each session aggregated from the embeddings of all the items contained in it. In this case, the user representation is different from each time point, as new sessions are added into the model along with the time. Other extensions include the incorporation of (1) variational inference into RNN to handle the uncertainty in sparse transaction data and simultaneously leverage the model's scalability on large real-world datasets BIB011  ; (2) other side information like item features and other contextual factors like time, location and interfaces into RNN-based models to enhance the recommendations BIB021 BIB002 ; (3) time decay or attention mechanism into RNN-based models to discriminate the intra-session dependency over items and thus achieve more precise recommendations  BIB012 ; and (4) traditional models like factorization machine or neighbourhood models to make up the drawbacks of RNN-only models BIB013 BIB005 . Other similar RNN-based models include BIB020 BIB014 BIB015 . (b) DNN-based Models. Except for RNN, DNN is an alternative solution for SBRS, especially when there is no strict order over items within a session. Here, DNN particularly refers to the naive deep neural network structure consisting of multi-layer perceptron (MLP). Next, we introduce a typical DNN-based SBRS and then discuss some variants. In BIB016 , a DNN is applied to learn a comprehensive session representation for recommendations by learning a optimized combination of the embeddings of different components under a session context with a MLP layer as below: where v s c , v s v are the embeddings of two sub sessions (i.e., click sub session and view sub session) which both serve as the context for target item prediction, while v i t and v u are the embeddings of the target item and the corresponding user respectively. W c , W v , W t and W u are the corresponding weight matrices to fully connect each of the embeddings to the first hidden layer of DNN. Similar to the above approach, Jannach etc. applied DNN to learn an optimized combination of different factors like "reminders", "item popularity" and "discount" as a compound session-based features for the next-item prediction BIB017 . Another example is the implementation of DNN to transfer and generalize the sparse user-item interactions to dense and informative session features for prediction. A wide liner models combined with a deep neural network is applied on Google Play to improve the mobile app recommendation performance BIB006 . In BIB007 , a DNN-based architecture was proposed to model both the long-term static and short-term temporal user preferences simultaneously for better recommendations. (c) CNN-based Models. CNN is another good choice for SBRS for two reasons: (1) It relaxes the rigid order assumption over items within a session commonly used by RNN, which makes the model more robust; (2) It has high capacity in learning local features from a certain area and relationships between different areas, which can effectively capture the union-level collective dependency that are usually ignored by other models. Next, we specifically introduce a representative CNN-based SBRS BIB022 and then demonstrate some other similar models. The model proposed in BIB022 mainly contains an embedding layer, convolutional layers and fully-connected layers. Particularly, the convolutional layers include a horizontal and a vertical convolutional layer respectively. Specifically, a filtering and a pooling operations are conducted in the horizontal convolutional layer successively. In the horizontal convolutional layer, the i th convolution value is achived by sliding one filter F k from top to bottom on the embedding matrix E to interacts with its horizontal dimensions: where ϕ c is the activation function for the convolutional layer. Then the final output o ∈ R n from the n filters is obtained by performing the max pooling on the convolution ] to capture the most significant features: Similarly, in the vertical convolutional layer, the convolution result is achieved by sliding the vertical filters on the embedding matrix E to interact with its columns. Finally, the outputs of the two convolutional filters are concatenated as the input of a fully-connected layer to get higher-level features for final predictions. Another similar work is a 3D CNN BIB018 built for SBRS, which jointly models the sequential patterns in session click data and the item characteristics from item content features. Furthermore, in BIB019 , the authors proposed a CNN model to capture the preference of individual users to generate personalized recommendations.