Abstract
Tag-aware recommender system leverages user-annotated historical data to enhance the understanding of user preferences and web service/item features, attracting widespread attention in academia and industry. However, most existing tag-aware recommender systems cannot effectively model the relationships among users, items, and tags, disrupting their comprehension of user preferences, item attributes, and tag semantics, thereby affecting recommendation performance. Therefore, we propose a tag-aware recommendation model based on attention mechanism and disentangled graph neural network (AM-DGNN). Specifically, we first construct three bipartite graphs describing user-tag, item-tag, and user-item relationships based on user-annotated historical data. Then, we utilize the multi-head attention mechanism on the first two relational graphs to integrate semantic information from tags into user and item representations, aiming to enhance the model’s understanding of user preferences and item features. Subsequently, on the user-item relational graph, we refine user and item feature representations to form intention subgraphs, describing the relationships between users and items under different intentions. Ultimately, we obtain intention-disentangled user and item representations to achieve the recommendation objective. Extensive experiments on two datasets demonstrate that the proposed model outperforms the baselines in tag-aware recommendation tasks.
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This research was supported by the National Natural Science Foundation of China under Grant No. 62372145 and 62202131.
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Yao, H., Yu, D., Wang, D., Zhang, H., Song, S., Li, J. (2024). Tag-Aware Recommendation Based on Attention Mechanism and Disentangled Graph Neural Network. In: Stefanidis, K., Systä, K., Matera, M., Heil, S., Kondylakis, H., Quintarelli, E. (eds) Web Engineering. ICWE 2024. Lecture Notes in Computer Science, vol 14629. Springer, Cham. https://doi.org/10.1007/978-3-031-62362-2_5
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