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CoGCN: co-occurring item-aware GCN for recommendation

  • S.I.: Evolutionary Computation based Methods and Applications for Data Processing
  • Published:
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Abstract

Graph convolution networks (GCNs) play an increasingly vital role in recommender systems, due to their remarkable relation modeling and representation capabilities. Concretely, they can capture high-order semantic correlations within sparse bipartite interaction graphs, thereby enhancing user–item collaborative encodings. Despite the exciting prospects, the existing GCN-based models mainly focus on user–item interactions and seldom consider effectiveness of the side item co-occurrence information on user behavior guidance, resulting in limited performance improvement. Therefore, we propose a novel side item co-occurrence information-aware GCN model. Specifically, we first decouple the original heterogeneous relation graph into corresponding user–item and item–item subgraphs for user–item interaction and item co-occurrence relation modeling. Thereafter, we conduct adaptive iterative aggregation on these subgraphs for user intention understanding and co-occurring item correlation perception. Finally, we present two semantic fusion strategies for sufficient user–item semantic collaborative learning, thereby boosting the overall recommendation performance. Extensive comparison experiments are conducted on three benchmark datasets to justify the superiority of our model.

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Data availability

The datasets generated and analyzed during the current study are available at https://github.com/garfieldcat1985/cogcn.

Notes

  1. https://github.com/garfieldcat1985/cogcn

  2. A symmetric matrix structure is convenient for the model implementation in programming practice.

  3. http://deepyeti.ucsd.edu/jianmo/amazon/index.html.

  4. https://www.tensorflow.org.

References

  1. Bordes A, Usunier N, Garcia-Duran A (2013) Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th conference and workshop on neural information processing systems, p 2787–2795

  2. Cao Y, Wang X, He X, Hu Z, Chua TS (2019) Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences. In: Proceedings of the 28th international conference on world wide web, IW3C2, p 151–161

  3. Chen T, Yin H, Ye G, Huang Z, Wang Y, Wang M (2020) Try this instead: personalized and interpretable substitute recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, ACM, p 891–900

  4. Cheng Z, Ding Y, He X, Zhu L, Song X, Kankanhalli MS (2018) A\(^3\)ncf: an adaptive aspect attention model for rating prediction. In: Proceedings of the 27th international joint conference on artificial intelligence, ijcai.org, p 3748–3754

  5. Cheng Z, Liu F, Mei S, Guo Y, Zhu L, Nie L (2021) Feature-level attentive ICF for recommendation. ACM Trans Inf Syst 40:1–24

    Article  Google Scholar 

  6. Deng X, Guan P, Hei C, Li F, Liu J, Xiong N (2021) An intelligent resource allocation scheme in energy harvesting cognitive wireless sensor networks. IEEE Trans Netw Sci Eng 8(2):1900–1912

    Article  MathSciNet  Google Scholar 

  7. Deng X, Li J, Guan P, Zhang L (2021) Energy-efficient UAV-aided target tracking systems based on edge computing. IEEE Internet Things J 9:2207–2214

    Article  Google Scholar 

  8. Deshpande M, Karypis G (2004) Item-based top-N recommendation algorithms. ACM Trans Inf Syst 1(1):143–177

    Article  Google Scholar 

  9. Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(7):2121–2159

    MathSciNet  MATH  Google Scholar 

  10. Fu B, Zhang W, Hu G, Dai X, Huang S, Chen J (2021) Dual side deep context-aware modulation for social recommendation. In: Proceedings of the 2021 world wide web conference on world wide web, ACM, p 2524–2534

  11. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics, p 249–256

  12. He X, Chen T, Kan MY, Chen X (2015) Trirank: reviewaware explainable rec-ommendation by modeling aspects. In: Proceedings of the 24th ACM international conference on information and knowledge management, ACM, p 1661–1670

  13. 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, IW3C2, p 173–182

  14. He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: simplifying and powering graph convo-lution network for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, ACM, p 639–648

  15. Hsiehz CK, Yangy L, Cuiy Y, Liny TY, Belongiey S, Estrin D (2017) Collaborative metric learning. In: Proceedings of the 26th international conference on world wide web, IW3C2, p 193–201

  16. Hu B, Shi C, Zhao WX, Yu PS (2018) Graph convolutional matrix completion. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, ACM

  17. Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representations

  18. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations

  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, ACM, p 59–66

  20. Liu F, Cheng Z, Zhu L, Liu C, Nie L (2020) A\(^2\)gcn: an attribute-aware attentive gcn model for recommendation. IEEE Trans Knowl Data Eng 34:4077–4088

    Article  Google Scholar 

  21. Liu F, Cheng Z, Liu Y, Zhu L, Gao Z, Nie L (2021a) Interest-aware message-passing GCN for recommendation. In: Proceedings of the 2021 world wide web conference on world wide web, ACM, p 1296–1305

  22. Liu S, Yu J, Deng X, Wan S (2021) Fedcpf: an efficient-communication federated learning approach for vehicular edge computing in 6g communication networks. IEEE Trans Intell Transp Syst 23:1616–1629

    Article  Google Scholar 

  23. Mao K, Zhu J, Xiao X, Lu B, Wang Z, He X (2021) Ultragcn: ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the 30th ACM international conference on information and knowledge management, ACM, p 1253–1261

  24. McAuley J, Targett C, Shi Q, Hengel AJVD (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, p 43–52

  25. Park C, Kim D, Oh J, Yu H (2017) Do “also-viewed” products help user rating prediction? In: Proceedings of the 2017 world wide web conference on world wide web, ACM, p 1113–1122

  26. Rendle S, Gantner Z, Freudenthaler C, Thieme LS (2011) Fast context-aware recommendations with factorization machines. In: Proceedings of the 34nd international ACM SIGIR conference on research and development in information retrieval, ACM, p 635–644

  27. Sun J, Zhang Y, Ma C, Coates M, Guo H, Tang R, He X (2019) Multi-graph convolution collaborative filtering. In: Proceedings of IEEE international conference on data mining, IEEE

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

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

  30. Wang X, Huang T, Wang D, Yuan Y, Liu Z, He X (2021) Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the 2021 world wide web conference on world wide web, ACM, p 878–887

  31. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th international joint conference on artificial intelligence, AAAI, p 1112–1119

  32. Wu B, Deng C, Guan B, Wang Y, Kangyang Y (2022) Enhancing sequential recommendation via decoupled knowledge graphs. The semantic web. Springer, Cham, pp 3–20

    Chapter  Google Scholar 

  33. Wu L, Yang Y, Zhang K, Hong R, Fu Y, Wang M (2020) Joint item recommendation and attribute inference: an adaptive graph convolutional network approach. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, ACM, p 679–688

  34. Xin X, He X, Zhang Y, Zhang Y, Jose J (2019) Relational collaborative filtering: modeling multiple item relations for recommendation. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, ACM, p 125–134

  35. Hu Y, Nie L, Liu M, Wang K, Wang Y, Hua X (2021) Coarse-to-fine semantic alignment for cross-modal moment localization. IEEE Trans Image Process 30:5933–5943

    Article  Google Scholar 

  36. Tang H, Zhu J, Wang L, Zheng Q, Zhang T (2022) Multi-level query interaction for temporal language grounding. IEEE Trans Intell Transp Syst 23:25479–25488

    Article  Google Scholar 

  37. Tang H, Zhu J, Liu M, Gao Z, Cheng Z (2022) Frame-wise cross-modal matching for video moment retrieval. IEEE Trans Multimed 24:1338–1349

    Article  Google Scholar 

  38. Xu M, Fu P, Liu B, Yin H, Li J (2021) A novel dynamic graph evolution network for salient object detection. Appl Intell 52:2854–2871

    Article  Google Scholar 

  39. Xu M, Fu P, Liu B, Li J (2021) Multi-stream attention-aware graph convolution network for video salient object detection. IEEE Trans Image Process 30:4183–4197

    Article  Google Scholar 

  40. Hu Y, Liu M, Su X, Gao Z, Nie L (2021) Video moment localization via deep cross-modal hashing. IEEE Trans Image Process 30:4667–4677

    Article  MathSciNet  Google Scholar 

  41. Hu Y, Zhan P, Xu Y, Zhao J, Li Y, Li X (2021) Temporal representation learning for time series classification. Neural Comput Appl 33:3169–3182

    Article  Google Scholar 

  42. Jin Y, Wang S, Liu F, Fan H, Hu Y, Li X, Liu S (2023) Deep temporal state perception towards artificial cyber-physical systems. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2023.3239413

    Article  Google Scholar 

  43. Yang B, Yih W, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the 3rd international conference on learning representations

  44. Zhang Y, Ai Q, Chen X, Wang P (2018) Learning over knowledge-base embeddings for recommendation. In: Proceedings of the 41st international ACM SIGIR conference on research and development in information retrieval, ACM

  45. Zhao X, Cheng Z, Zhu L, Zheng J, Li X (2021) Ugrec: modeling directed and undirected relations for recommendation. In: Proceedings of the 44nd international ACM SIGIR conference on research and development in information retrieval, ACM, p 193–202

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Acknowledgements

This work was supported in part by the Key R &D Program of Shandong Province, China (Major Scientific and Technological Innovation Projects), No.:2022CXGC020107; in part by the National Natural Science Foundation (NSF) of China, No.:62276155, No.:62206156, No.:72004127, and No.:62206157; in part by the NSF of Shandong Province, No.:ZR2021MF040 and No.:ZR2022QF047; in part by the Alibaba Group through Alibaba Innovative Research Program, No.:21169774.

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, 250101, Shandong, China

    Xinxiao Zhao, Hao Liu, Mingzhu Xu, Haoyu Tang, Xueqing Li & Yupeng Hu

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Fan Liu

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  1. Xinxiao Zhao
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  2. Fan Liu
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  3. Hao Liu
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Correspondence to Yupeng Hu.

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Zhao, X., Liu, F., Liu, H. et al. CoGCN: co-occurring item-aware GCN for recommendation. Neural Comput & Applic 35, 25107–25120 (2023). https://doi.org/10.1007/s00521-023-08703-w

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