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A self-supervised graph-learning method for reliable-relation identification in social recommendation

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Abstract

As social commerce continues to develop, personalized recommender systems increasingly leverage social networks to understand consumer interests and aid in consumption decisions. Despite the widespread use of graph neural networks in existing social recommendation methods, a significant challenge persists: social relationships do not always represent similar interests between users. This reduces the efficacy of social recommendation methods. To address this challenge, we propose a novel self-supervised graph-learning method for reliable-relation identification in social recommendation (SGRI). In SGRI, the primary social recommendation task uses graph neural networks to learn social influence and collaborative interests from the social and interaction graphs, respectively. An auxiliary self-supervised learning task aims to identify reliable relations in these graphs, thereby enhancing the primary task’s performance. This auxiliary task employs an adaptive data-augmentation strategy based on user-item-friend triadic relations to generate diverse graph views, providing users and items with credible neighborhoods. Subsequently, a local-local contrastive pretext method is used for the node self-discrimination across different graph views, and a local-context contrastive pretext method ensures interest similarity between users and their social circles. Experimental results show that our proposed SGRI method consistently outperforms the state-of-the-art methods on three real-world datasets.

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Data Availability

Data are available in the following public domain resources: The LastFM dataset: https://files.grouplens.org/datasets/hetrec2011/ The Yelp dataset: https://github.com/Coder-Yu/QRec The Epinions dataset: https://www.cse.msu.edu/~tangjili/datasetcode/truststudy.htm

References

  1. Li, N., Gao, C., Jin, D., Liao, Q.: Disentangled modeling of social homophily and influence for social recommendation. IEEE Trans. Knowl. Data Eng. 35(6), 5738–5751 (2023)

    MATH  Google Scholar 

  2. Yu, L., Leng, Y., Zhang, D., He, S.: Collaborative group embedding and decision aggregation based on attentive influence of individual members: A group recommendation perspective. Decis. Support Syst. 165, 113894 (2023)

    Article  MATH  Google Scholar 

  3. Bhattacharyya, S., Bose, I.: S-commerce: Influence of facebook likes on purchases and recommendations on a linked e-commerce site. Decis. Support Syst. 138, 113383 (2020)

    Article  Google Scholar 

  4. Jiang, X., Sun, H., Chen, Y., He, L.: Ktpgn: Novel event-based group recommendation method considering implicit social trust and knowledge propagation. Inf. Sci. 642, 119159 (2023)

    Article  Google Scholar 

  5. Chang, H.H., Lu, Y.-Y., Lin, S.C.: An elaboration likelihood model of consumer respond action to facebook second-hand marketplace: Impulsiveness as a moderator. Inf. Manag. 57(2), 103171 (2020)

    Article  MATH  Google Scholar 

  6. Sedera, D., Lokuge, S., Atapattu, M., Gretzel, U.: Likes—the key to my happiness: The moderating effect of social influence on travel experience. Inf. Manag. 54(6), 825–836 (2017)

    Article  Google Scholar 

  7. Han, X., Wang, L., Crespi, N., Park, S., Cuevas, Á.: Alike people, alike interests? inferring interest similarity in online social networks. Decis. Support Syst. 69, 92–106 (2015)

    Article  Google Scholar 

  8. Liu, H., Chen, Y., Li, P., Zhao, P., Wu, X.: Enhancing review-based user representation on learned social graph for recommendation. Knowl.-Based Syst. 266, 110438 (2023)

    Article  MATH  Google Scholar 

  9. Qian, F., Qin, K., Chen, H., Chen, J., Zhao, S., Zhang, Y.: Adaptive social recommendation combined with the multi-domain influence. Inf. Syst. 113, 102145 (2023)

    Article  MATH  Google Scholar 

  10. Wang, X.-J., Liu, T., Fan, W.: Tgvx: Dynamic personalized poi deep recommendation model. INFORMS J. Comput. 35, 711–908 (2023)

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhang, H., Gan, M.: Mbdl: Exploring dynamic dependency among various types of behaviors for recommendation. Inf. Syst. 124, 102407 (2024)

    Article  Google Scholar 

  12. Gan, M., Xu, G., Ma, Y.: A multi-behavior recommendation method exploring the preference differences among various behaviors. Expert Syst. Appl. 228, 120316 (2023)

    Article  MATH  Google Scholar 

  13. Wang, X., He, X., Wang, M., Feng, F., Chua, T.-S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174 (2019)

  14. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 639–648 (2020)

  15. Hao, P., Qian, Z., Wang, S., Bai, C.: Community aware graph embedding learning for item recommendation. World Wide Web 26(6), 4093–4108 (2023)

    Article  MATH  Google Scholar 

  16. Gan, M., Zhang, H.: Viga: A variational graph autoencoder model to infer user interest representations for recommendation. Inf. Sci. 640, 119039 (2023)

    Article  MATH  Google Scholar 

  17. Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., et al.: A survey of graph neural networks for recommender systems: Challenges, methods, and directions. ACM Transactions on Recommender Systems 1, 1–51 (2023)

    Article  MATH  Google Scholar 

  18. Li, H., Li, L., Xv, G., Lin, C., Li, K., Jiang, B.: Spex: A generic framework for enhancing neural social recommendation. ACM Trans. Inf. Syst. (TOIS) 40(2), 1–33 (2021)

    Article  MATH  Google Scholar 

  19. Xiao, X., Wen, J., Zhou, W., Luo, F., Gao, M., Zeng, J.: Multi-interaction fusion collaborative filtering for social recommendation. Expert Syst. Appl. 205, 117610 (2022)

    Article  Google Scholar 

  20. Liao, J., Zhou, W., Luo, F., Wen, J., Gao, M., Li, X., Zeng, J.: Sociallgn: Light graph convolution network for social recommendation. Inf. Sci. 589, 595–607 (2022)

    Article  Google Scholar 

  21. Yoo, E., Gu, B., Rabinovich, E.: Diffusion on social media platforms: A point process model for interaction among similar content. J. Manag. Inf. Syst. 36(4), 1105–1141 (2019)

    Article  MATH  Google Scholar 

  22. Cialdini, R.B., Goldstein, N.J.: Social influence: Compliance and conformity. Annu. Rev. Psychol. 55, 591–621 (2004)

    Article  MATH  Google Scholar 

  23. Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., Wang, M.: A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 235–244 (2019)

  24. Wu, L., Li, J., Sun, P., Hong, R., Ge, Y., Wang, M.: Diffnet++: A neural influence and interest diffusion network for social recommendation. IEEE Trans. Knowl. Data Eng. 34(10), 4753–4766 (2020)

    Article  MATH  Google Scholar 

  25. Gan, M., Wang, C., Yi, L., Gu, H.: Exploiting dynamic social feedback for session-based recommendation. Inf. Process. Manag. 61(3), 103632 (2024)

    Article  MATH  Google Scholar 

  26. Gan, M., Kwon, O.-C.: A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domains. Knowl.-Based Syst. 251, 109158 (2022)

    Article  MATH  Google Scholar 

  27. Zhang, J., Li, Y., Zou, R., Zhang, J., Jiang, R., Fan, Z., Song, X.: Hyper-relational knowledge graph neural network for next poi recommendation. World Wide Web 27(4), 1–19 (2024)

    Article  MATH  Google Scholar 

  28. Gan, M., Ma, Y.: Mapping user interest into hyper-spherical space: A novel poi recommendation method. Inf. Process. Manag. 60(2), 103169 (2023)

    Article  Google Scholar 

  29. Ahmadian, S., Joorabloo, N., Jalili, M., Ren, Y., Meghdadi, M., Afsharchi, M.: A social recommender system based on reliable implicit relationships. Knowl.-Based Syst. 192, 105371 (2020)

    Article  Google Scholar 

  30. Yu, J., Gao, M., Li, J., Yin, H., Liu, H.: Adaptive implicit friends identification over heterogeneous network for social recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 357–366 (2018)

  31. Divyaa, L., Pervin, N.: Towards generating scalable personalized recommendations: Integrating social trust, social bias, and geo-spatial clustering. Decis. Support Syst. 122, 113066 (2019)

    Article  Google Scholar 

  32. Wu, L., Lin, H., Tan, C., Gao, Z., Li, S.Z.: Self-supervised learning on graphs: Contrastive, generative, or predictive. IEEE Trans. Knowl. Data Eng. 35(04), 4216–4235 (2023)

    Article  MATH  Google Scholar 

  33. Liu, Y., Xia, L., Huang, C.: Selfgnn: Self-supervised graph neural networks for sequential recommendation. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1609–1618 (2024)

  34. Yang, F., Peng, D., Zhang, S.: Self-supervised hypergraph neural network for session-based recommendation supported by user continuous topic intent. Appl. Soft Comput. 111406 (2024)

  35. Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., Xie, X.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726–735 (2021)

  36. Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 135–142 (2010)

  37. Wang, X., Lu, W., Ester, M., Wang, C., Chen, C.: Social recommendation with strong and weak ties. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 5–14 (2016)

  38. Zhang, C., Wang, Y., Zhu, L., Song, J., Yin, H.: Multi-graph heterogeneous interaction fusion for social recommendation. ACM Trans. Inf. Syst. (TOIS) 40(2), 1–26 (2021)

    MATH  Google Scholar 

  39. Zhuang, J., Meng, S., Zhang, J., Sheng, V.S.: Contrastive learning based graph convolution network for social recommendation. ACM Trans. Knowl. Discov. Data 17(8), 1–21 (2023)

    Article  MATH  Google Scholar 

  40. Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: Proceedings of the Web Conference 2021, pp. 2069–2080 (2021)

  41. Velickovic, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: Proceedings of 7th International Conference on Learning Representations, pp. 1–17 (2019)

  42. Zhou, K., Wang, H., Zhao, W.X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., Wen, J.-R.: S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 1893–1902 (2020)

  43. Zhang, W., Lin, Y., Liu, Y., You, H., Wu, P., Lin, F., Zhou, X.: Self-supervised reinforcement learning with dual-reward for knowledge-aware recommendation. Appl. Soft Comput. 131, 109745 (2022)

    Article  MATH  Google Scholar 

  44. Yu, J., Yin, H., Gao, M., Xia, X., Zhang, X., Viet Hung, N.Q.: Socially-aware self-supervised tri-training for recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2084–2092 (2021)

  45. Yu, J., Yin, H., Li, J., Wang, Q., Hung, N.Q.V., Zhang, X.: Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: Proceedings of the Web Conference 2021, pp. 413–424 (2021)

  46. Wang, L., Xu, X., Ouyang, K., Duan, H., Lu, Y., Zheng, H.-T.: Self-supervised dual-channel attentive network for session-based social recommendation. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 2034–2045 (2022)

  47. Long, X., Huang, C., Xu, Y., Xu, H., Dai, P., Xia, L., Bo, L.: Social recommendation with self-supervised metagraph informax network. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, pp. 1160–1169 (2021)

  48. Wang, T., Xia, L., Huang, C.: Denoised self-augmented learning for social recommendation. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pp. 2324–2331 (2023)

  49. Ren, X., Xia, L., Yang, Y., Wei, W., Wang, T., Cai, X., Huang, C.: Sslrec: A self-supervised learning framework for recommendation. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 567–575 (2024)

  50. Jiao, Y., Xiong, Y., Zhang, J., Zhang, Y., Zhang, T., Zhu, Y.: Sub-graph contrast for scalable self-supervised graph representation learning. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 222–231 (2020). IEEE Computer Society

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Nos.72271024, 71871019) and 2021 Tencent Wechat Rhino-Bird Focused Research Program Research.

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Authors and Affiliations

  1. School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China

    Hang Zhang & Mingxin Gan

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  1. Hang Zhang
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Contributions

Hang Zhang proposed the research idea, developed the methodology, implemented the algorithm and computer code, performed data analysis, and wrote and revised the manuscript. Mingxin Gan formulated the research goal, supervised the research activities, revised the manuscript, and secured financial support.

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Correspondence to Mingxin Gan.

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Zhang, H., Gan, M. A self-supervised graph-learning method for reliable-relation identification in social recommendation. World Wide Web 28, 19 (2025). https://doi.org/10.1007/s11280-025-01330-6

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