mlsanityData to 7 Oct 2026

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

arXiv 2111.07378 ↗First posted 14 Nov 2021cs.IR cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Sequential recommendation aims to choose the most suitable items for a user at a specific timestamp given historical behaviors. Existing methods usually model the user behavior sequence based on the transition-based methods like Markov Chain. However, these methods also implicitly assume that the users are independent of each other without considering the influence between users. In fact, this influence plays an important role in sequence recommendation since the behavior of a user is easily affected by others. Therefore, it is desirable to aggregate both user behaviors and the influence between users, which are evolved temporally and involved in the heterogeneous graph of users and items. In this paper, we incorporate dynamic user-item heterogeneous graphs to propose a novel sequential recommendation framework. As a result, the historical behaviors as well as the influence between users can be taken into consideration. To achieve this, we firstly formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences. After that, we exploit the conditional random field to aggregate the heterogeneous graphs and user behaviors for probability estimation, and employ the pseudo-likelihood approach to derive a tractable objective function. Finally, we provide scalable and flexible implementations of the proposed framework. Experimental results on three real-world datasets not only demonstrate the effectiveness of our proposed method but also provide some insightful discoveries on sequential recommendation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 55 references that are in the map. First posted on arXiv 14 Nov 2021.

Builds on

32 of 55 references.

References · 32

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  7. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  8. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  9. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  10. 2018Self-Attentive Sequential Recommendationcited by 1,169
  11. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  12. 2017Neural Collaborative Filteringcited by 1,108
20 more
  1. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  2. 2019Heterogeneous Graph Attention Networkcited by 732
  3. 2019Graph Neural Networks for Social Recommendationcited by 711
  4. 2020Heterogeneous Graph Transformercited by 581
  5. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 473
  6. 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
  7. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
  8. 2017Recurrent Neural Networks with Top-k Gains for Session-based Recommendationscited by 165
  9. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  10. 2017Fast Matrix Factorization for Online Recommendation with Implicit Feedbackcited by 100
  11. 2017Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networkscited by 84
  12. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  13. 2017Translation-based Recommendationcited by 70
  14. 2019Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendationcited by 70
  15. 2020Enhancing Social Recommendation with Adversarial Graph Convolutional Networkscited by 16
  16. 2021Dynamic Memory based Attention Network for Sequential Recommendationcited by 16
  17. 2019DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender Systemcited by 15
  18. 2020Semi-Supervised Disentangled Framework for Transferable Named Entity Recognitioncited by 7
  19. 2021Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphscited by 2
  20. 2019Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Modelscited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2.

4 papers.

  1. 2024PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrievalcited by 4
  2. 2022REST: Debiased Social Recommendation via Reconstructing Exposure Strategiescited by 1
  3. 2025Adaptive Long-term Embedding with Denoising and Augmentation for Recommendationcited by 1
  4. 2025Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbackscited by 1