mlsanityData to 14 Oct 2026

Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender

arXiv 2306.00009 ↗First posted 29 May 2023cs.LGcited by 0

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Abstract (arXiv)

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level diversity are both important for industrial recommender systems. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity. Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is deployed to increase individual-level diverisity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate attributes. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diveristy. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Offline simulations and online A/B tests show our solution can effectively improve both user engagement and system revenue.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 29 May 2023.

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References · 18

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  3. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  4. 2017Neural Collaborative Filteringcited by 1,111
  5. 2016Wide & Deep Learning for Recommender Systemscited by 747
  6. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
  7. 2019Neural Graph Collaborative Filteringcited by 661
  8. 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
  9. 2019Multi-Interest Network with Dynamic Routing for Recommendation at Tmallcited by 114
  10. 2019Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Predictioncited by 54
  11. 2022DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generationcited by 33
  12. 2020COLD: Towards the Next Generation of Pre-Ranking Systemcited by 31
6 more
  1. 2021DGCN: Diversified Recommendation with Graph Convolutional Networkscited by 29
  2. 2022Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendationcited by 19
  3. 2021Sliding Spectrum Decomposition for Diversified Recommendationcited by 15
  4. 2020Graph Convolution Machine for Context-aware Recommender Systemcited by 13
  5. 2021Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Networkcited by 8
  6. 2020Personalized Re-ranking for Improving Diversity in Live Recommender Systemscited by 4

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