结合用户共同意图及社交关系的群组推荐方法

doi:10.3778/j.issn.1673-9418.2304025

摘要/Abstract

摘要： 已有的群组推荐模型，在求解用户表示时大多比较单调且仅简单利用用户间的社交关系，使得用户表示不够准确，并且大都未考虑用户共同意图以及社交关系对群组偏好的影响，导致推荐的项目很难符合用户的需求。基于此，提出一种结合用户共同意图及社交关系的群组推荐模型（GR-UCISI）。首先构造用户-项目交互历史与社交关系相结合的用户意图分离模型，利用图神经网络采集每个用户的用户-项目交互以及社交关系信息，求解用户意图和项目表示；其次利用网络游走算法与[K-means]聚类算法将用户分组，结合用户群组、用户意图以及群组意图聚合过程获取群组共同意图表示；最后根据群组共同意图表示与项目表示得出群组推荐项目列表。该方法充分考虑到用户的个性以及群组成员间的共性对群组偏好的影响，同时结合社交关系缓解数据稀疏性问题，提升模型性能。实验结果表明，与9个对比模型中推荐效果最好的模型相比，在Gowalla数据集上，GR-UCISI的Precision和NDCG指标值分别提高3.01%和5.26%；在Yelp-2018数据集上，GR-UCISI的Precision和NDCG指标值分别提高2.96%和1.12%。

关键词: 群组推荐, 用户共同意图, 社交关系, 图神经网络

Abstract: Existing group recommendation models often have a monotonous approach when solving user representation, and only simple social relationships between users are utilized. This makes user representation inaccurate and most models do not consider the impact of user common intention and social interaction on group preferences. As a result, recommended items are not aligned with user needs. To address these issues, a new group recommendation model based on user common intention and social interaction (GR-UCISI) is proposed. Firstly, a user intention separation model that combines user-item interaction history with social interaction is constructed. Graph neural networks are utilized to collect user-item interaction and social interaction information, and to solve user intention and item representation. Secondly, by utilizing the social network random walk algorithm and the [K-means] clustering algorithm, users can be grouped. User group, user intention and group intention aggregation process are combined to obtain group common intention representation. Finally, group common intention representation and item representation are calculated to obtain the list of recommended items for the group. This method fully considers the impact of user individuality and commonality among group members on group preferences. It also utilizes social relationships to alleviate the problem of data sparsity and improve model performance. The experimental results show that compared with the model with the best recommendation effect of nine models, on the Gowalla dataset, the Precision and NDCG of the GR-UCISI model are increased by 3.01% and 5.26% respectively, on the Yelp-2018 dataset, the Precision and NDCG of the GR-UCISI model are increased by 2.96% and 1.12% respectively.

Key words: group recommendation, user common intention, social interaction, graph neural network

钱忠胜, 张丁, 李端明, 王亚惠, 姚昌森, 俞情媛. 结合用户共同意图及社交关系的群组推荐方法[J]. 计算机科学与探索, 2024, 18(5): 1368-1382.

QIAN Zhongsheng, ZHANG Ding, LI Duanming, WANG Yahui, YAO Changsen, YU Qingyuan. Group Recommendation Model Based on User Common Intention and Social Interaction[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(5): 1368-1382.

参考文献

[1] AL-SHAMRI M Y H. Power coefficient as a similarity measure for memory based collaborative recommender systems[J]. Expert Systems with Applications, 2014, 41(13): 5680-5688.
[2] HE X N, DENG K, WANG X, et al. LightGCN: simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 25-30, 2020. New York: ACM, 2020: 639-648.
[3] MASSA P, AVESANI P. Trust-aware recommender systems[C]//Proceedings of the 2007 ACM Conference on Recommender Systems, Minneapolis, Oct 19-20, 2007. New York: ACM, 2007: 17-24.
[4] WANG X, HE X N, WANG M, et al. Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, Jul 21-25, 2019. New York: ACM, 2019: 165-174.
[5] YANNAM V R, KUMAR J, BABU K S, et al. Enhancing the accuracy of group recommendation using slope one[J]. The Journal of Supercomputing, 2023, 79(1): 499-540.
[6] WANG Y, DAI Z, CAO J, et al. Intra-and inter-association attention network-enhanced policy learning for social group recommendation[J]. World Wide Web, 2023, 26(1): 71-94.
[7] TANJIM M M, SU C, BENJAMIN E, et al. Attentive sequential models of latent intent for next item recommendation[C]//Proceedings of the Web Conference 2020, Taipei, China, Apr 20-24, 2020. New York: ACM, 2020: 2528-2534.
[8] AMER-YAHIA S, ROY S B, CHAWLAT A, et al. Group recommendation: semantics and efficiency[J]. Proceedings of the VLDB Endowment, 2010, 2(1): 754-765.
[9] LIU X J, TIAN Y, MAO Y, et al. Exploring personal impact for group recommendation[C]//Proceedings of the 21st ACM International Conference on Information and Knowledge Management, Maui, Oct 29-Nov 2, 2012. New York: ACM, 2012: 674-683.
[10] LENG Y, YU L. Incorporating global and local social networks for group recommendations[J]. Pattern Recognition, 2022, 127: 108601.
[11] CHEN T, YIN H, CHEN H, et al. AIR: attentional intention-aware recommender systems[C]//Proceedings of the 35th IEEE International Conference on Data Engineering, Macao, China, Apr 8-11, 2019. Piscataway: IEEE, 2019: 304-315.
[12] LI H Y, WANG X, ZHANG Z W, et al. Intention-aware sequential recommendation with structured intent transition[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 34(11): 5403-5414.
[13] CHEN T, YIN H, LONG J, et al. Thinking inside the box: learning hypercube representations for group recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 1664-1673.
[14] DENG X, LIAO G, ZENG Y. Group event recommendation based on a heterogeneous attribute graph considering long-and short-term preferences[J]. Journal of Intelligent Information Systems, 2023, 61(1): 271-297.
[15] LIANG R, ZHANG Q, WANG J, et al. A hierarchical attention network for cross-domain group recommendation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(3): 3859-3873.
[16] YANNAM V R, KUMAR J, BABU K S, et al. Improving group recommendation using deep collaborative filtering approach[J]. International Journal of Information Technology, 2023,15(3): 1-9.
[17] KUMAR C, CHOWDARY C R. A study on the role of uninterested items in group recommendations[J]. Electronic Com-merce Research, 2022(21): 1-27.
[18] YUAN Q, CONG G, LIN C Y. COM: a generative model for group recommendation[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, Aug 24-27, 2014. New York: ACM, 2014: 163-172.
[19] TRAN L V, PHAM T A N, TAY Y, et al. Interact and decide: medley of sub-attention networks for effective group recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, Jul 21-25, 2019. New York: ACM, 2019: 255-264.
[20] LIAO G Q, HUANG X M, XIONG N X, et al. Softwarized attention-based context-aware group recommendation technology in event-based industrial cyber-physical systems[J]. IEEE Transactions on Industrial Informatics, 2021, 17(11): 6894-6905.
[21] WANG X, JIN H Y, ZHANG A, et al. Disentangled graph collaborative filtering[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 25-30, 2020. New York: ACM, 2020: 1001-1010.
[22] LI K, WANG C D, LAI J H, et al. Self-supervised group graph collaborative filtering for group recommendation[C]//Proceedings of the 16th ACM International Conference on Web Search and Data Mining, Singapore, Feb 27-Mar 3, 2023. New York: ACM, 2023: 69-77.
[23] DU Y, MENG X, ZHANG Y, et al. GERF: a group event recommendation framework based on learning-to-rank[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(4): 674-687.
[24] MEI L, REN P, CHEN Z, et al. An attentive interaction network for context-aware recommendations[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 157-166.
[25] CHEN W Y, REN P J, CAI F, et al. Improving end-to-end sequential recommendations with intent-aware diversification[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Oct 19-23, 2020. New York: ACM, 2020: 175-184.
[26] 潘一腾, 何发智, 于海平. 一种基于信任关系隐含相似度的社会化推荐算法[J]. 计算机学报, 2018, 41(1): 65-81.
PAN Y T, HE F Z, YU H P. Social recommendation algorithm using implicit similarity in trust[J]. Chinese Journal of Computers, 2018, 41(1): 65-81.
[27] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, Jun 18-21, 2009: 452-461.