JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (3): 54-65.doi: 10.6040/j.issn.1671-9352.1.2024.040

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Attribute enhanced temporary group recommendation algorithm fusing long-and short-term interests

WANG Zhixuan1, PANG Jifang1,2*, WANG Zhiqiang1,2, SONG Peng3, LI Ru1,2   

  1. 1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China;
    2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, Shanxi, China;
    3. School of Economics and Management, Shanxi University, Taiyuan 030006, Shanxi, China
  • Published:2026-03-18

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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

CLC Number: 

  • TP391
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