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Non-pairwise Collaborative Filtering

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Abstract

On the users’ interaction graph, neighbors have been widely explored in the embedding function of collaborative filtering to address the sparsity issue. However, the embedding learning models are highly subject to the following pairwise interaction function on interest prediction. We argue that the core of personalized recommendation locates interaction rather than embeddings. Distinct from the sparse pairwise interactions, there are a large amount of inherent non-pairwise signals hidden among neighbors, which are promising for interaction learning. In this work, we explore the active effect of non-pairwise neighbors on the target user-item pair and propose a non-pairwise collaborative filtering (NPCF) model. For a target user-item pair, NPCF mines target-aware CF signals of neighbors by aggregating both pairwise and non-pairwise CF signals of the target for a target-specific interaction embedding. Experiments on three real-world datasets demonstrate that NPCF outperforms the state-of-the-art models for personalized recommendation. It implies NPCF is capable of learning interactions with non-pairwise neighbors.

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References

  1. Chen M, Li Y, Zhou X (2021) Conet:co-occurrence neural networks for recommendation. Futur Gener Comput Syst 124:308–314

    Article  Google Scholar 

  2. Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: International conference on machine learning (ICML 2020), pp 1725–1735

  3. Chen Q, Zhao H, Li W, Huang P, Ou W (2019) Behavior sequence transformer for e-commerce recommendation in alibaba. In: Proceedings of the 1st international workshop on deep learning practice for high-dimensional sparse data, pp 1–4

  4. Da’u A, Salim N, Idris R (2021) Multi-level attentive deep user-item representation learning for recommendation system. Neurocomputing 433:119–130

    Article  Google Scholar 

  5. Du Y, Wang L, Peng Z, Guo W (2021) Review-based hierarchical attention cooperative neural networks for recommendation. Neurocomputing 447:38–47

    Article  Google Scholar 

  6. Fan W, Ma Y, Li Q, He Y, Zhao YE, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: The World wide web conference, WWW 2019, San Francisco, CA, USA, May 13–17, 2019. ACM, pp 417–426

  7. Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems, pp 1025–1035

  8. Harper FM, Konstan JA (2015) The movielens datasets: history and context. ACM Trans Interact Intell Syst (TIIS) 5(4):1–19

    Google Scholar 

  9. He R, McAuley JJ (2016) Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th international conference on world wide web, WWW 2016, Montreal, Canada, April 11–15, 2016. ACM, pp 507–517

  10. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182

  11. Hu B, Shi C, Zhao WX, Yu PS (2018) Leveraging meta-path based context for top-N recommendation with a neural co-attention model. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2018, London, UK, August 19–23, 2018. ACM, pp 1531–1540

  12. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on Recommender systems, pp 135–142

  13. Jian M, Guo J, Zhang C, Jia T, Huo L (2021) Semantic manifold modularization-based ranking for image recommendation. Pattern Recognit 3:108100

  14. Jian M, Zhang C, Liu M, Fu X, Li S, Shi G, Wu L (2022) Siamese graph-based dynamic matching for collaborative filtering. Inf Sci 611:185–198

    Article  Google Scholar 

  15. Jiang Y, Ma H, Liu Y, Li Z, Chang L (2021) Enhancing social recommendation via two-level graph attentional networks. Neurocomputing 449:71–84

    Article  Google Scholar 

  16. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24–26, 2017, conference track proceedings. OpenReview.net

  17. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  18. Li C, Hu L, Shi C, Song G, Lu Y (2021) Sequence-aware heterogeneous graph neural collaborative filtering. In: Proceedings of the 2021 SIAM international conference on data mining (SDM). SIAM, pp 64–72

  19. Liang D, Altosaar J, Charlin L, Blei DM (2016) Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM conference on recommender systems, RecSys’16, pp 59–66. Association for Computing Machinery, New York, NY, USA

  20. Ni Y, Chen X, Pan W, Chen Z, Ming Z (2021) Factored heterogeneous similarity model for recommendation with implicit feedback. Neurocomputing 455:59–67

    Article  Google Scholar 

  21. Shi C, Hu B, Zhao WX, Philip SY (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357–370

    Article  Google Scholar 

  22. Shi C, Li Y, Zhang J, Sun Y, Philip SY (2016) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17–37

    Article  Google Scholar 

  23. Shi C, Ye Y, Zhang J (2019) HENA 2019: The 3rd workshop of heterogeneous information network analysis and applications. In: Proceedings of the 28th ACM international conference on information and knowledge management, CIKM 2019, Beijing, China, November 3–7, 2019. ACM, pp 2991–2992

  24. Sun Y, Yuan NJ, Xie X, McDonald K, Zhang R (2017) Collaborative intent prediction with real-time contextual data. ACM Trans Inf Syst (TOIS) 35(4):1–33

    Article  Google Scholar 

  25. Velickovic P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30–May 3, 2018, conference track proceedings. OpenReview.net

  26. Wang H, Zhang F, Hou M, Xie X, Guo M, Liu Q (2018) Shine: signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 592–600

  27. Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018) Ripplenet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 417–426

  28. Wang X, He X, Cao Y, Liu M, Chua TS (2019) Kgat: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 950–958

  29. Wang X, He X, Wang M, Feng F, Chua TS (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 165–174

  30. Wang X, Wang R, Shi C, Song G, Li Q (2020) Multi-component graph convolutional collaborative filtering. In: The Thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, the thirty-second innovative applications of artificial intelligence conference, IAAI 2020, the tenth AAAI symposium on educational advances in artificial intelligence, EAAI 2020, New York, NY, USA, February 7–12, 2020. AAAI Press, pp 6267–6274

  31. Wu F, de Souza AH, Zhang T, Fifty C, Yu T, Weinberger KQ (2019) Simplifying graph convolutional networks. In: International conference on machine learning (ICML 2019)

  32. Xue F, He X, Wang X, Xu J, Liu K, Hong R (2019) Deep item-based collaborative filtering for top-n recommendation. ACM Trans In Syst (TOIS) 37(3):1–25

    Article  Google Scholar 

  33. Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 974–983

  34. Zhang F, Yuan NJ, Lian D, Xie X, Ma WY (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 353–362

  35. Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2018, London, UK, August 19–23, 2018. ACM, pp 1059–1068

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 62176011 and 61976010, Inner Mongolia Autonomous Region Science and Technology Foundation under Grant No. 2021GG0333, and Beijing Postdoctoral Research Foundation under Grant NO. Q6042001202101.

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  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Meng Jian, Chenlin Zhang, Tuo Wang & Lifang Wu

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  1. Meng Jian
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  2. Chenlin Zhang
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  3. Tuo Wang
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  4. Lifang Wu
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Correspondence to Lifang Wu.

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Jian, M., Zhang, C., Wang, T. et al. Non-pairwise Collaborative Filtering. Neural Process Lett 55, 7627–7648 (2023). https://doi.org/10.1007/s11063-023-11277-2

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