融合长短期兴趣的属性增强临时群组推荐算法

doi:10.6040/j.issn.1671-9352.1.2024.040

摘要/Abstract

摘要： 群组推荐旨在为群体用户提供推荐服务,其最终目的是满足群组成员间不同的偏好需求。现有群组推荐算法大多是面向固定群组的,忽视了大量具有偶然性和特殊性的临时群组。为了进一步拓展群组推荐算法的应用场景,有效应对临时群组历史交互信息短缺的问题,提出一种融合长短期兴趣的属性增强临时群组推荐算法。首先,在用户—项目整体历史交互和用户短期交互序列中分别注入项目的属性信息,综合运用超图网络、图神经网络和门控循环单元学习用户的长短期兴趣。进而,利用注意力机制将组内成员的长期兴趣聚合为群组的长期兴趣;同时,设计成员短期兴趣与群组长期兴趣之间的相似性度量策略计算成员权重,并通过加权融合的方式获得群组的短期兴趣。在此基础上,采用长短期兴趣对比学习最大化两类群组兴趣之间的一致性,通过对比损失和推荐损失对模型进行联合优化,以获得高质量的群组综合表征,实现临时群组的精准推荐。最后,通过2个真实数据集上的对比分析和消融实验验证所提模型的可行性和有效性,实验结果表明项目属性信息和用户短期兴趣可有效增强临时群组表征质量,显著提升模型的推荐效果。

关键词: 项目属性信息, 长短期兴趣, 超图网络, 对比学习, 群组推荐

Abstract: Group recommendation aims to provide recommendation services for group users, with the ultimate goal of meeting the different preference needs of group members. Most existing group recommendation algorithms are designed for fixed groups, ignoring a large number of temporary groups with randomness and specificity. In order to further expand the application scenarios of group recommendation algorithms and effectively address the shortage of historical interaction information in temporary groups, attribute enhanced temporary group recommendation algorithm fusing long-and short-term interests(ALSTG)is proposed. Firstly, item attributes information are injected into the overall historical interactions between users and items and the users short-term interaction sequences, respectively. Then, users long-and short-term interests are learned by comprehensively using hypergraph networks, graph neural networks, and gated recurrent units. Furthermore, the long-term interests of members are aggregated into the long-term interests of group through attention mechanism. At the same time, a similarity measurement strategy between the short-term interests of members and the long-term interests of the group is designed to calculate the weights of members, and the short-term interests of the group are obtained through weighted fusion. On this basis, a long-and short-term interest contrastive learning approach is adopted to maximize the consistency between the two types of group interests. The model is jointly optimized through comparative loss and recommendation loss to obtain high-quality group comprehensive representation, thereby achieving accurate recommendation for temporary groups. Finally, the feasibility and effectiveness of the proposed model were verified through comparative analysis and ablation experiments on two real datasets. The experimental results show that item attribute information and user short-term interests can effectively enhance the quality of temporary group representation and significantly improve the recommendation performance of the model.

Key words: item attribute information, long-and short-term interests, hypergraph networks, contrastive learning, group recommendation

中图分类号:

TP391

王智玄,庞继芳,王智强,宋鹏,李茹. 融合长短期兴趣的属性增强临时群组推荐算法[J]. 《山东大学学报(理学版)》, 2026, 61(3): 54-65.

WANG Zhixuan, PANG Jifang, WANG Zhiqiang, SONG Peng, LI Ru. Attribute enhanced temporary group recommendation algorithm fusing long-and short-term interests[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2026, 61(3): 54-65.

参考文献

[1] SHYAM A, KUMAR V, KAGITA V R, et al. UniRecSys: a unified framework for personalized, group, package, and package-to-group recommendations[J]. Knowledge-Based Systems, 2024, 289:111552.
[2] FU Shengxi, REN Qianqian, LYU Xingfeng, et al. Multi-behavior recommendation with SVD graph neural networks[J]. Expert Systems with Applications, 2024, 249:123575.
[3] HU Zheng, CAI Shi-Min, WANG Jun, et al. Collaborative recommendation model based on multi-modal multi-view attention network: movie and literature cases[J]. Applied Soft Computing, 2023, 144:110518.
[4] MENG Lingqiang, LIU Zhizhong, CHU Dianhui, et al. POI recommendation for occasional groups based on hybrid graph neural networks[J]. Expert Systems with Applications, 2024, 237:121583.
[5] GUO Lei, YIN Hongzhi, CHEN Tong, et al. Hierarchical hyperedge embedding-based representation learning for group recommendation[J]. ACM Transactions on Information Systems, 2021, 40(1):1-27.
[6] YANG Mingdai, LIU Zhiwei, YANG Liangwei, et al. Ranking-based group identification via factorized attention on social tripartite graph[C] //Proceedings of the 16th ACM International Conference on Web Search and Data Mining. Singapore: ACM, 2023:769-777.
[7] WU Xixi, XIONG Yun, ZHANG Yao, et al. Consrec: learning consensus behind interactions for group recommendation[C] //Proceedings of the ACM Web Conference 2023. Texas: ACM, 2023:240-250.
[8] SANKAR A, WU Y H, WU Y H, et al. Groupim: a mutualinformation maximization framework for neural group recommendation[C] //Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. Xian: ACM, 2020:1279-1288.
[9] GUO Lei, YIN Hongzhi, WANG Qinyong, et al. Group recommendation with latent voting mechanism[C] //Proceedings of the 36th International Conference on Data Engineerin. Dallas: IEEE, 2020:121-132.
[10] HE Z X, CHOW C Y, ZHANG J D. GAME: learning graphical and attentive multi-view embeddings for occasional group recommendation[C] //Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Xian: ACM, 2020:649-658.
[11] ZHENG Xiaolin, TAN Yanchao, WANG Yan, et al. Finding high-quality item attributes for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 35(8):7980-7993.
[12] HUANG Ling, CHEN Biyi, YE Haiyi, et al. DIAG: a deep interaction-attribute-generation model for user-generated item recommendation[J]. Knowledge-Based Systems, 2022, 243:108463.
[13] XU Li, ZENG Jun, PENG Weile, et al. Modeling and predicting user preferences with multiple item attributes for sequential recommendations[J]. Knowledge-Based Systems, 2023, 260:110174.
[14] SANJAY K, PERVIN N. Integration of short and long-term interests: a preference aware session-based recommender[J]. Neurocomputing, 2024, 583:127558.
[15] FU Jiarun, GAO Rong, YU Yonghong, et al. Contrastive graph learning long and short-term interests for POI recommendation[J]. Expert Systems with Applications, 2024, 238:121931.
[16] SONG Jing, SHEN Hong, OU Zijing, et al. ISLF: interest shift and latent factors combination model for session-based recommendation[C] //Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao: AAAI, 2019:5765-5771.
[17] ZHAO Na, LONG Zhen, WANG Jian, et al. AGRE: a knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder[J]. Knowledge-Based Systems, 2023, 259:110078.
[18] YU Dongjin, YU Ting, WANG Dongjing, et al. Long tail service recommendation based on cross-view and contrastive learning[J]. Expert Systems with Applications, 2024, 238:121957.
[19] WANG Yu, ZHAO Yuying, ZHANG Yi, et al. Collaboration-aware graph convolutional network for recommender systems[C] //Proceedings of the ACM Web Conference 2023. Texas: ACM, 2023:91-101.
[20] ZENG Biqing, CHI Junlong, HONG Peilin, et al. Context-aware graph embedding with gate and attention for session-based recommendation[J]. Neurocomputing, 2024, 574:127221.
[21] XIA Lianghao, HUANG Chao, XU Yong, et al. Hypergraph contrastive collaborative filtering[C] //Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid: ACM, 2022:70-79.
[22] WU Xixi, XIONG Yun, ZHANG Yao, et al. Dual intents graph modeling for user-centric group discovery[C] //Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Birmingham: ACM, 2023:2716-2725.
[23] GU Shuyun, WANG Xiao, SHI Chuan, et al. Self-supervised graph neural networks for multi-behavior recommendation[C] //Proceedings of the 30th International Joint Conference on Artificial Intelligence. Montreal: Morgan Kaufmann, 2022:2052-2058.
[24] YANG Yuhao, HUANG Chao, XIA Lianghao, et al. Knowledge graph contrastive learning for recommendation[C] //Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid: ACM, 2022:1434-1443.
[25] ZHAO Pengpeng, LUO Anjing, LIU Yanchi, et al. Where to go next: a spatio-temporal gated network for next POI recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(5):2512-2524.
[26] YAN Cairong, WANG Yiwei, ZHANG Yanting, et al. Modeling long and short-term user behaviors for sequential recommendation with deep neural networks[C] //2021 International Joint Conference on Neural Networks. Rome: IEEE, 2021:1-8.
[27] WU Shu, TANG Yuyuan, ZHU Yanqiao, et al. Session-based recommendation with graph neural networks[C] //Proceedings of the AAAI Conference on Artificial Intelligence. Vancouver: AAAI, 2019, 33(1):346-353.
[28] JIA Renqi, ZHOU Xiaofei, DONG Linhua, et al. Hypergraph convolutional network for group recommendation[C] //2021 IEEE International Conference on Data Mining. Auckland: IEEE, 2021:260-269.
[29] FENG Yifan, YOU Haoxuan, ZHANG Zizhao, et al. Hypergraph neural networks[C] //Proceedings of the AAAI Conference on Artificial Intelligence. Vancouver: AAAI, 2019:3558-3565.
[30] WU F, SOUZA A H, ZHANG T Y, et al. Simplifying graph convolutional networks[C] //International Conference on Machine Learning. California: ACM, 2019:6861-6871.
[31] HE Xiangnan, DENG Kuan, WANG Xiang, 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. Xian: ACM, 2020:639-648.
[32] GUTMANN M, HYVARINEN A. Noise-contrastive estimation: a new estimation principle for unnormalized statistical models[C] //Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. Sardinia: JMLR, 2010:297-304.
[33] HE Wei, SUN Guohao, LU Jinhu, et al. Candidate-aware graph contrastive learning for recommendation[C] //Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Taipei: ACM, 2023:1670-1679.
[34] YU Junliang, YIN Hongzhi, XIA Xin, et al. Are graph augmentations necessary? Simple graph contrastive learning for recommendation[C] //Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid: ACM, 2022:1294-1303.
[35] CREMONESI P, KOREN Y, TURRIN R. Performance of recommender algorithms on top-n recommendation tasks[C] //Proceedings of the 4th ACM Conference on Recommender Systems. Barcelona: ACM, 2010:39-46.
[36] HE Xiangnan, LIAO Lizi, ZHANG Hanwang, et al. Neural collaborative filtering[C] //Proceedings of the 26th International Conference on World Wide Web. Perth: ACM, 2017:173-182.
[37] CAO Da, HE Xiangnan, MIAO Lianhai, et al. Attentive group recommendation[C] //The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. Ann Arbor: ACM, 2018:645-654.
[38] ZHANG Song, ZHENG Nan, WANG Danli. HetGRec: heterogeneous graph attention network for group recommendation[J]. IEEE Intelligent Systems, 2022, 38(1):9-18.
[39] HONG T, IBRAHIM N F. Group recommendation fueled by noise-based graph contrastive learning[J]. Journal of King Saud University-Computer and Information Sciences, 2024, 36(5):102063.

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