Abstract
Conversational recommender systems (CRS) can dynamically capture user fine-grained preference by directly asking whether a user likes an attribute or not. However, like traditional recommender systems, accurately comprehending users’ preferences remains a critical challenge for CRS to make effective conversation policy decisions. While there have been various efforts made to improve the performance of CRS, they have neglected the impact of the users’ social context, which has been proved to be valuable in modeling user preferences and enhancing the performance of recommender systems. In this paper, we propose a social-enhanced user preference estimation model (SocialCRS) to leverage the social context of users to better learn user embedding representation. Specifically, we construct a user-item-attribute heterogeneous graph and apply a graph convolution network (GCN) to learn the embeddings of users, items, and attributes. Another GCN is used on the user social context graph to learn the social embedding of users. To estimate better user preference, the attention mechanism is adopted to aggregate the embedding of the user’s friends. By aggregating these users’ embeddings, we obtain social-enhanced user preferences. Through extensive experiments on two public benchmark datasets in a multi-round conversational recommendation scenario, we demonstrate the effectiveness of our model, which significantly outperforms the state-of-the-art CRS methods.
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References
Cao, D., He, X., Miao, L., Xiao, G., Chen, H., Xu, J.: Social-enhanced attentive group recommendation. IEEE Trans. Knowl. Data Eng. 33(3), 1195–1209 (2021)
Chen, H., et al.: Large-scale interactive recommendation with tree-structured policy gradient. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3312–3320 (2019)
Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Model. User-Adap. Inter. 22(1), 125–150 (2012)
Christakopoulou, K., Beutel, A., Li, R., Jain, S., Chi, E.H.: Q &R: a two-stage approach toward interactive recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 139–148 (2018)
Deng, Y., Li, Y., Sun, F., Ding, B., Lam, W.: Unified conversational recommendation policy learning via graph-based reinforcement learning. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1431–1441 (2021)
Fan, W., et al.: Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417–426 (2019)
Gao, C., Lei, W., He, X., de Rijke, M., Chua, T.S.: Advances and challenges in conversational recommender systems: a survey. AI Open 2, 100–126 (2021)
Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 123–129 (2015)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1725–1731 (2017)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)
He, X., Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 355–364 (2017)
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)
Jannach, D., Manzoor, A., Cai, W., Chen, L.: A survey on conversational recommender systems. ACM Comput. Surv. 54(5), 1–36 (2021)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations (2017)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Lei, W., et al.: Estimation-action-reflection: towards deep interaction between conversational and recommender systems. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 304–312 (2020)
Lei, W., He, X., de Rijke, M., Chua, T.S.: Conversational recommendation: formulation, methods, and evaluation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2425–2428 (2020)
Lei, W., et al.: Interactive path reasoning on graph for conversational recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2073–2083 (2020)
Li, R., Kahou, S., Schulz, H., Michalski, V., Charlin, L., Pal, C.: Towards deep conversational recommendations. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 9748–9758 (2018)
Luo, K., Yang, H., Wu, G., Sanner, S.: Deep critiquing for VAE-based recommender systems. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1269–1278 (2020)
Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940 (2008)
Marsden, P.V., Friedkin, N.E.: Network studies of social influence. Sociol. Meth. Res. 22(1), 127–151 (1993)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Pu, P., Faltings, B.: Decision tradeoff using example-critiquing and constraint programming. Constraints 9(4), 289–310 (2004)
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000 (2010)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)
Sun, Y., Zhang, Y.: Conversational recommender system. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 235–244 (2018)
Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3(4), 1113–1133 (2013)
Wang, H., Wu, Q., Wang, H.: Factorization bandits for interactive recommendation. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
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)
Xu, K., Yang, J., Xu, J., Gao, S., Guo, J., Wen, J.R.: Adapting user preference to online feedback in multi-round conversational recommendation. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 364–372 (2021)
Zhang, Y., Chen, X., Ai, Q., Yang, L., Croft, W.B.: Towards conversational search and recommendation: system ask, user respond. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 177–186 (2018)
Zhou, S., et al.: Interactive recommender system via knowledge graph-enhanced reinforcement learning. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 179–188 (2020)
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Gao, Z., Wei, L., Zhou, W., Lin, M., Hu, S. (2023). A Method of Social Context Enhanced User Preferences for Conversational Recommender Systems. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10476. Springer, Cham. https://doi.org/10.1007/978-3-031-36027-5_15
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