Sequential Recommendation with User Memory Networks

Chen, Xu; Xu, Hongteng; Zhang, Yongfeng; Tang, Jiaxi; Cao, Yixin; Qin, Zheng; Zha, Hongyuan

doi:10.1145/3159652.3159668

Cited by 446 publications

(253 citation statements)

References 25 publications

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“…Therefore, another key challenge in SRSs is to learn sequential dependencies attentively and discriminatively over user-item interaction sequences with noise. Quite a few works have attempted to solve such a typical issue by employing the attention models [19] or memory networks [1] to selectively retain and utilize information from those interactions that are truly relevant to the next interaction prediction. The technical progress achieved in these solutions will be presented in Section 3.3.…”

Section: Handling User-item Interaction Sequences With Noisementioning

confidence: 99%

“…Besides the basic RNN structure, some variants are proposed to capture more complex dependencies in a sequence, like hierarchical RNN [13]. However, RNN is not flawless for SRSs, with the shortcomings in two aspects: (1) it is easy to generate fake dependencies due to the overly strong assumption that any adjacent interactions in a sequence must be dependent, which may not be the cases in the real world because there are usually irrelevant or noisy interactions inside a sequence; and (2) it is likely to capture the point-wise dependencies only while ignoring the collective dependencies (e.g., several interactions collaboratively affect the next one).…”

Section: Basic Deep Neural Networkmentioning

confidence: 99%

“…Memory networks are introduced into SRSs to capture the dependencies between any historical user-item interaction and the next one directly by incorporating an external memory matrix. Such matrix enables it possible to store and update the historical interactions in a sequence more explicitly and dynamically to improve the expressiveness of the model and reduce the interference of those irrelevant interactions [1]. Furthermore, some works incorporate a key-value memory network to store and update the corresponding knowledge base information of the interacted items in a sequence to learn the attribute level preference for the enhancement of recommendations [11].…”

Section: Advanced Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Sequential Recommender Systems: Challenges, Progress and Prospects

Wang

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

296

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The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users' preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.

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Section: Handling User-item Interaction Sequences With Noisementioning

confidence: 99%

Section: Basic Deep Neural Networkmentioning

confidence: 99%

Section: Advanced Modelsmentioning

confidence: 99%

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Sequential Recommender Systems: Challenges, Progress and Prospects

Wang

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

296

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“…Chen et al [3] introduces the memory mechanism to sequential recommender systems, which designs a user memory-augmented neural network (MANN) to express feature-level interests. As for more fine-grained user preference, Huang et al [8,9] use knowledge base information to enhance the semantic representation of key-value memory network called knowledge enhanced sequential recommender.…”

Section: Sequence-aware Recommendationmentioning

confidence: 99%

SDM

Jin

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

115

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Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching approaches in industry. However, they are not effective to model dynamic and evolving preferences of users. In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors. Compared with existing sequence-aware recommendation methods, we tackle the following two inherent problems in realworld applications: (1) there could exist multiple interest tendencies in one session.(2) long-term preferences may not be effectively fused with current session interests. Long-term behaviors are various and complex, hence those highly related to the short-term session should be kept for fusion. We propose to encode behavior sequences with two corresponding components: multi-head self-attention module to capture multiple types of interests and long-short term gated fusion module to incorporate long-term preferences. Successive items are recommended after matching between sequential user behavior vector and item embedding vectors. Offline experiments on real-world datasets show the superior performance of the proposed SDM. Moreover, SDM has been successfully deployed on online large-scale recommender system at Taobao and achieves improvements in terms of a range of commercial metrics.

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“…To reduce information overload and satisfy the diverse needs of users, personalized recommender systems come into being and play more and more important roles in modern society. These systems can provide personalized experiences, serve huge service demands, and bring significant benefits to at least two parties: (1) help users easily discover products that they are interested in; (2) create opportunities for product providers to increase the revenue.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Gating Networks for Sequential Recommendation

Peng

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

249

121

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The chronological order of user-item interactions is a key feature in many recommender systems, where the items that users will interact may largely depend on those items that users just accessed recently. However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the shortterm user interests given several items the user just accessed. To cope with these challenges, we propose a hierarchical gating network (HGN), integrated with the Bayesian Personalized Ranking (BPR) to capture both the long-term and short-term user interests. Our HGN consists of a feature gating module, an instance gating module, and an item-item product module. In particular, our feature gating and instance gating modules select what item features can be passed to the downstream layers from the feature and instance levels, respectively. Our item-item product module explicitly captures the item relations between the items that users accessed in the past and those items users will access in the future. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on five real-world datasets. The experimental results demonstrate the effectiveness of our model on Top-N sequential recommendation. CCS CONCEPTS• Information systems → Recommender systems.

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Sequential Recommendation with User Memory Networks

Cited by 446 publications

References 25 publications

Sequential Recommender Systems: Challenges, Progress and Prospects

Sequential Recommender Systems: Challenges, Progress and Prospects

SDM

Hierarchical Gating Networks for Sequential Recommendation

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