Abstract
Incorporating social relations in recommendation provides a promising way to alleviate problems of sparsity and cold start in collaborative filtering methods. However, most existing methods do not yet take into account social relations in a relative complete way. Besides the differences between preferences of friends, connection strength and expertise differences of users on a given item also have impacts on the spread of preference between friends. In this paper, we propose a social-aware recommendation model named Multi-Attention Item Recommendation model based on Social relations (MAIRS) which allows us to select more informative friends from the perspectives of their preferences, connection strengths, and expertise on items by their own respective attention models. And then, the three attention models are fused together by utilizing an aggregation function. We compare our method with state-of-the-art models on three real-world datasets: Delicious, Ciao and Epinions. The experimental results show that our method consistently outperforms state-of-the-art models in terms of several ranking metrics.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473 (2014)
Beigi, G., Liu, H.: Similar but different: exploiting users’ congruity for recommendation systems. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds.) SBP-BRiMS 2018. LNCS, vol. 10899, pp. 129–140. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93372-6_15
Berkani, L.: SSCF: a semantic and social-based collaborative filtering approach. In: AICCSA, pp. 1–4. IEEE (2015)
Chen, C., Zhang, M., Liu, Y., Ma, S.: Social attentional memory network: modeling aspect- and friend-level differences in recommendation. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, pp. 177–185, 11–15 February 2019
Cocking, D., Kennett, J.: Friendship and the self. Ethics 108(3), 502–527 (1998)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, pp. 191–198, 15–19 September 2016
Granovetter, M.S.: The Strength of Weak Ties. In: Social networks, pp. 347–367. Elsevier, Cambridge (1977)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, pp. 855–864, 13–17 August 2016
He, X., Chua, T.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, pp. 355–364, 7–11 August 2017
He, X., He, Z., Song, J., Liu, Z., Jiang, Y., Chua, T.: NAIS: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354–2366 (2018)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web WWW 2017, Perth, Australia, pp. 173–182, 3–7 April 2017
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, Barcelona, Spain, pp. 135–142, 26–30 September 2010
Karatzoglou, A., Hidasi, B.: Deep learning for recommender systems. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, pp. 396–397, 27–31 August 2017
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)
Onnela, J.P., et al.: Structure and tie strengths in mobile communication networks. Proc. Nat. Acad. Sci. 104(18), 7332–7336 (2007)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, pp. 452–461, 18–21 June 2009
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 19 (2009)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)
Vinh, T.D.Q., Pham, T.N., Cong, G., Li, X.: Attention-based group recommendation. CoRR abs/1804.04327 (2018)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, pp. 3156–3164, 7–12 June 2015
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 5:1–5:38 (2019)
Zhao, T., McAuley, J.J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, pp. 261–270, 3–7 November 2014
Acknowledgements
This work was partly supported by the National Natural Science Foundation of China under Grant No. 61572002, No. 61170300, No. 61690201, and No. 61732001.
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Li, Y., Mu, K. (2019). Multi-attention Item Recommendation Model Based on Social Relations. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_8
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