Sequential Recommendation with User Memory Networks

By Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, Hongyuan Zha. 2018

544 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xing Xie
2. Yongfeng Zhang
3. Xu Chen
4. Xiangnan He
5. Wayne Xin Zhao
6. Ji-rong Wen
7. Hongwei Wang
8. Wenjie Li

In Information Systems

1. Yongfeng Zhang
2. Xu Chen
3. Xiangnan He
4. Xing Xie
5. Wayne Xin Zhao
6. Ji-rong Wen
7. Fei Sun
8. Miao Zhao

Citations in the corpus

• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, background): “Researchers have also proposed using memory networks in other tasks such as sentiment classification [13, 27] and recommendation [5, 8].”
• Explainable Recommendation: A Survey and New Perspectives (2018, background): “Recently, there has been some related work trying to generate natural language explanations, and the basic idea is to train sequence-tosequence models based on user reviews, and to generate “review-like” sentences as explanations of the recommendation, such as Costa et al. [2017] and Chen et al.”
• Dynamic Explainable Recommendation Based on Neural Attentive Models (2019, methodology): “NARRE (Chen et al. 2018a) leveraged attention mechanism to extract valuable item reviews to explain the rating prediction process.”
• Filter-enhanced MLP is All You Need for Sequential Recommendation (2022, background): “,memory network [9, 20] and self-attention mechanism [26, 27]) to enhance the modeling capacity for effectively capturing dynamic user preference.”
• Sequential Recommender Systems: Challenges, Progress and Prospects (2019, background): “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.”
• Contrastive Learning for Sequential Recommendation (2022, background): “Chen et al. [5] and Huang et al. [22] leverage the memory-augmented neural network to store and update useful information explicitly.”
• Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (2019, background): “Several works[3, 10, 16, 27] utilize memory network for user interest modeling.”
• Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks (2018, methodology): “It has been shown that MNs are effective in memorizing long-term data characteristics [3], which can even evolve and update over time.”
• Hierarchical Gating Networks for Sequential Recommendation (2019, result): “hibits promising performance in sequence learning and is utilized in sequential recommendation. In [18], Kang et al. propose to leverage self-attention for adaptively considering interacted items. In [1, 16], memory networks [33] are adopted to memorize the important items that will play a role in p”
• Small Sample Learning in Big Data Era (2018, methodology): “…successfully applied to visual question answering(VQA) (Li et al., 2018c), semantic image segmentation (Luo et al., 2017a), image-to-image translation (Zhu et al., 2017b; Yi et al., 2017), zero-shot visual recognition (Chen et al., 2018b), and neural machine translation (He et al., 2016a, 2017a).”
• Deep Session Interest Network for Click-Through Rate Prediction (2019, methodology): “[ Tang and Wang, 2018 ] uses convolutional neural network and [Chen et al. , 2018] adopts user memory network to enhances the expressiveness of the sequential model.”
• Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit (2019, background): “Most recently, the memory network has also drawn attentions from user recommendation tasks [6, 9, 33].”
• Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation (2019, background): “Explainable recommendation essentially aims to solve the problem of "why an item is recommended to a user", which is important in a practical system due to its benefits on enhancing the recommendation perverseness as well as the users’ satisfaction [8, 43, 45, 47].”
• In2Rec: Influence-based Interpretable Recommendation (2019, background): “Recently, due to the powerful ability in representation learning, deep learning is adopted in recommendation to model textual content and generate the explanations [7–9 , 29].”
• Controllable Multi-Interest Framework for Recommendation (2020, methodology): “Recent researches [7, 27, 36] formalize the recommender system as a sequential recommendation problem.”
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (2019, background): “Other than recurrent neural networks, various deep learning models are also introduced for sequential recommendation [3, 22, 33, 49].”
• Gromov-Wasserstein Learning for Graph Matching and Node Embedding (2019, methodology): “Additionally, we can apply the inexact proximal point method (Xie et al., 2018; Chen et al., 2018a), running one-step Sinkhorn-Knopp projection in each inner iteration.”