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Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels

arXiv 2102.06950 ↗First posted 13 Feb 2021cs.AI cs.IRcited by 0

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Abstract (arXiv)

Online gaming is growing faster than ever before, with increasing challenges of providing better user experience. Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types. Although many sequential RS already exist, they are mainly single-sequence, single-task, and single-user-level. In this paper, we introduce a new sequential recommendation model for multiple sequences, multiple tasks, and multiple user levels (abbreviated as M3Rec) in Tencent Games platform, which can fully utilize complex data in online games. We leverage Graph Neural Network and multi-task learning to design M3Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games. We verify the effectiveness of M3Rec on three online games of Tencent Games platform, in both offline and online evaluations. The results show that M3Rec successfully addresses the challenges of recommendation in online games, and it generates superior recommendations compared with state-of-the-art sequential recommendation approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 72 references that are in the map. First posted on arXiv 13 Feb 2021.

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References · 35

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  3. 2017A Survey on Multi-Task Learningcited by 838
  4. 2015Gated Graph Sequence Neural Networkscited by 792
  5. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  6. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  7. 2017Neural Attentive Session-based Recommendationcited by 237
  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. 2019A Survey on Session-based Recommender Systemscited by 97
  12. 2019Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferencescited by 87
23 more
  1. 2016Improved Recurrent Neural Networks for Session-based Recommendationscited by 86
  2. 2017Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networkscited by 84
  3. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  4. 2017Translation-based Recommendationcited by 70
  5. 2019Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendationcited by 70
  6. 2018Sequence-Aware Recommender Systemscited by 58
  7. 2018RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendationcited by 51
  8. 2019Rethinking the Item Order in Session-based Recommendation with Graph Neural Networkscited by 43
  9. 2019Jointly Learning Explainable Rules for Recommendation with Knowledge Graphcited by 40
  10. 2018Evaluation of Session-based Recommendation Algorithmscited by 37
  11. 2018Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Taskscited by 24
  12. 2020GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendationcited by 24
  13. 2013Using Temporal Data for Making Recommendationscited by 20
  14. 2019RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systemscited by 13
  15. 2017Contextual Sequence Modeling for Recommendation with Recurrent Neural Networkscited by 11
  16. 2017Session-aware Information Embedding for E-commerce Product Recommendationcited by 9
  17. 2018LRMM: Learning to Recommend with Missing Modalitiescited by 8
  18. 2019Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluationscited by 7
  19. 2019Distributed Algorithms for Fully Personalized PageRank on Large Graphscited by 4
  20. 2018TransRev: Modeling Reviews as Translations from Users to Itemscited by 2
  21. 2015From Group Recommendations to Group Formationcited by 1
  22. 2017Recurrent Latent Variable Networks for Session-Based Recommendationcited by 1
  23. 2019Cold-start Playlist Recommendation with Multitask Learningcited by 1

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