A Comprehensive Review of Group Recommendation Methods Based on Deep Learning

被引：0

作者：

Zheng, Nan ^{[1
,2
]}

Zhang, Song ^{[1
,2
]}

Liu, Yu-Qiao ^{[1
,2
]}

Wang, Yu-Tong ^{[3
]}

Wang, Fei-Yue ^{[2
,3
]}

机构：

[1] State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing

[2] School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing

[3] State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2024年 / 50卷 / 12期

基金：

中国国家自然科学基金;

关键词：

deep learning; Group recommendation; group representation learning; recommender system overview;

D O I：

10.16383/j.aas.c230781

中图分类号：

学科分类号：

摘要：

Group recommendation has emerged as a highly active research topic in the fields of information retrieval and data mining in recent years. Its objective is to select a group of items from a large candidate set that is likely to be of interest to a set of users. With the advancement of deep learning, numerous group recommendation methods based on deep learning have been proposed. This paper provides a brief introduction to the background knowledge of this problem. It reviews the methods of data acquisition and conducts a comprehensive review, systematic classification, and in-depth analysis of group recommendation algorithms based on deep learning. In addition, this paper outlines some group recommendation datasets and evaluation methods suitable for deep methods, and conducts comparative experimental analysis and discussion on various recommendation algorithms. Finally, the research challenges in this field were analyzed, and valuable future research directions were discussed. © 2024 Science Press. All rights reserved.

引用

页码：2301 / 2324

页数：23

共 89 条

[1]

Jia-Rui Tang, Ran Tian, Dan-Hui Liang, In-depth report on the E-commerce industry: Deepening online consumption mindset and actively exploring new consumption scenarios

[2]

Zhang Hong-Lei, Li Yi-Dong, Wu Jun, Chen Nai-Yue, Dong Hai-Rong, A survey on privacy-preserving federated recommender systems, Acta Automatica Sinica, 48, 9, pp. 2142-2163, (2022)

[3]

Yang Y, Zhang C B, Song X, Dong Z, Zhu H S, Li W J., Contextualized knowledge graph embedding for explainable talent training course recommendation, ACM Transactions on Information Systems, 42, 2, (2024)

[4]

Cheng M Y, Liu Q, Zhang W Y, Liu Z D, Zhao H K, Chen E H., A general tail item representation enhancement framework for sequential recommendation, Frontiers of Computer Science, 18, 6, (2024)

[5]

Hao B W, Yin H Z, Li C P, Chen H., Self-supervised graph learning for occasional group recommendation, International Journal of Intelligent Systems, 37, 12, pp. 10880-10902, (2022)

[6]

McCarthy J F, Anagnost T D., MusicFX: An arbiter of group preferences for computer supported collaborative workouts, Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 363-372, (1998)

[7]

Xu Xiao-Ming, Mei Hong-Yan, Yu Heng, Li Xiao-Hui, Review of group recommendation methods based on preference fusion, Journal of Chinese Computer Systems, 41, 12, pp. 2500-2508, (2020)

[8]

Zhang Yu-Jie, Du Yu-Lu, Meng Xiang-Wu, Research on group recommender systems and their applications, Chinese Journal of Computers, 39, 4, pp. 745-764, (2016)

[9]

Zhao Hai-Yan, Cheng Ruo-Yao, Chen Qing-Kui, Cao Jian, Group recommender system: Current status and future trends, Journal of Chinese Computer Systems, 42, 6, pp. 1144-1151, (2021)

[10]

Dara S, Chowdary C R, Kumar C., A survey on group recommender systems, Journal of Intelligent Information Systems, 54, 2, pp. 271-295, (2020)

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