Side-information integrated sequential recommendation incorporates supplementary information to alleviate the issue of data sparsity. The state-of-the-art works mainly leverage some side information to improve the attention calculation to learn user representation more accurately. However, there are still some limitations to be addressed in this topic. Most of them merely learn the user representation at the item level and overlook the association of the item sequence and the side-information sequences when calculating the attentions, which results in the incomprehensive learning of user representation. Some of them learn the user representations at both the item and side-information levels, but they still face the problem of insufficient optimization of multiple user representations. To address these limitations, we propose a novel model, i.e., Multi-Sequence Sequential Recommender (MSSR), which learns the user's multiple representations from diverse sequences. Specifically, we design a multi-sequence integrated attention layer to learn more attentive pairs than the existing works and adaptively fuse these pairs to learn user representation. Moreover, our user representation alignment module constructs the self-supervised signals to optimize the representations. Subsequently, they are further refined by our side information predictor during training. For item prediction, our MSSR extra considers the side information of the candidate item, enabling a comprehensive measurement of the user's preferences. Extensive experiments on four public datasets show that our MSSR outperforms eleven state-of-the-art baselines. Visualization and case study also demonstrate the rationality and interpretability of our MSSR.

In recent years, sequential recommendation (SR) has gained significant attention in the recommender systems community. However, most previous works only consider the (user, item, timestep) interaction sequences, which limits the recommendation performance. To overcome this limitation, some studies have utilized user reviews to enrich the understanding of user preferences. However, existing review-based sequential recommendation (RBSR) methods only use either a user’s review on items or an item’s reviews by users, overlooking their complementary nature. In addition, most existing RBSR methods use a simple dot-product operation between the embeddings of a user and the candidate items for scoring, which may not adequately capture the complex relationships among the item sequence, review sequence and candidate items. To release the potential of RBSR, we propose a novel model called cascaded cross attention (CCA), which utilizes aggregated reviews to compensate for the information that is lacking in individual reviews. Moreover, we propose a cascaded cross-attention layer to better capture the dependency intra a sequence and the relationships between a sequence and the candidate items. Extensive experimental results on three public datasets demonstrate that our CCA outperforms the state-of-the-art methods. Additionally, the case study and visualization results showcase high interpretability of our CCA.

Heterogeneous sequential recommendation that models sequences of items associated with more than one type of feedback such as examinations and purchases is an emerging topic in the research community, which is also an important problem in many real-world applications. Though there are some methods proposed to exploit different types of feedback in item sequences such as RLBL, RIB, and BINN, they are based on RNN and may not be very competitive in capturing users’ complex and dynamic preferences. And most existing advanced sequential recommendation methods such as the CNN- and attention-based methods are often designed for making use of item sequences with one single type of feedback, which thus can not be applied to the studied problem directly. As a response, we propose a novel feedback-aware local and global (FLAG) preference learning model for heterogeneous sequential recommendation. Our FLAG contains four modules, including (i) a local preference learning module for capturing a user’s short-term interest, which adopts a novel feedback-aware self-attention block to distinguish different types of feedback; (ii) a global preference learning module for modeling a user’s global preference; (iii) a local intention learning module, which takes a user’s real feedback in the next step, i.e., the user’s intention at the current step, as the query vector in a self-attention block to figure out the items that match the user’s intention well; and (iv) a prediction module for preference integration and final prediction. We then conduct extensive experiments on three public datasets and find that our FLAG significantly outperforms 13 very competitive baselines in terms of two commonly used ranking-oriented metrics in most cases. We also include ablation studies and sensitivity analysis of our FLAG to have more in-depth insights.

The bias problems in recommender systems are an important challenge. In this paper, we focus on solving the bias problems via uniform data. Previous works have shown that simple modeling with a uniform data can alleviate the bias problems and improve the performance. However, the uniform data is usually few and expensive to collect in a real product. In order to use the valuable uniform data more effectively, we propose a novel and general knowledge distillation framework for counterfactual recommendation with four specific methods, including label-based distillation, feature-based distillation, sample-based distillation and model structure-based distillation. Moreover, we discuss the relation between the proposed framework and the previous works. We then conduct extensive experiments on both public and product datasets to verify the effectiveness of the proposed four methods. In addition, we explore and analyze the performance trends of the proposed methods on some key factors, and the changes in the distribution of the recommendation lists. Finally, we emphasize that counterfactual modeling with uniform data is a rich research area, and list some interesting and promising research topics worthy of further exploration. Note that the source codes are available athttps://github.com/dgliu/TKDE_KDCRec.

Recently, sequential recommendation has become a research hotspot while multi-behavior sequential recommendation (MBSR) that exploits users' heterogeneous interactions in sequences has received relatively little attention. Existing works often overlook the complementary effect of different perspectives when addressing the MBSR problem. In addition, there are two specific challenges remained to be addressed. One is the heterogeneity of a user's intention and the context information, the other one is the sparsity of the interactions of target behavior. To release the potential of multi-behavior interaction sequences, we propose a novel framework named NextIP that adopts a dual-task learning strategy to convert the problem to two specific tasks, i.e., next-item prediction and purchase prediction. For next-item prediction, we design a target-behavior aware context aggregator (TBCG), which utilizes the next behavior to guide all kinds of behavior-specific item sub-sequences to jointly predict the next item. For purchase prediction, we design a behavior-aware self-attention (BSA) mechanism to extract a user's behavior-specific interests and treat them as negative samples to learn the user's purchase preferences. Extensive experimental results on two public datasets show that our NextIP performs significantly better than the state-of-the-art methods.

Most methods for context-aware recommendation focus on improving the feature interaction layer, but overlook the embedding layer. However, an embedding layer with random initialization often suffers in practice from the sparsity of the contextual features, as well as the interactions between the users (or items) and context. In this paper, we propose a novel user-event graph embedding learning (UEG-EL) framework to address these two sparsity challenges. Specifically, our UEG-EL contains three modules: 1) a graph construction module is used to obtain a user-event graph containing nodes for users, intents and items, where the intent nodes are generated by applying intent node attention (INA) on nodes of the contextual features; 2) a user-event collaborative graph convolution module is designed to obtain the refined embeddings of all features by executing a new convolution strategy on the user-event graph, where each intent node acts as a hub to efficiently propagate the information among different features; 3) a recommendation module is equipped to integrate some existing context-aware recommendation model, where the feature embeddings are directly initialized with the obtained refined embeddings. Moreover, we identify a unique challenge of the basic framework, that is, the contextual features associated with too many instances may suffer from noise when aggregating the information. We thus further propose a simple but effective variant, i.e., UEG-EL-V, in order to prune the information propagation of the contextual features. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our UEG-EL and its variant.