Multibehavior Intent Disentangled Learning for Fine-Grained Interest Discovery in Recommendation
Yuqing Du, Guan Yuan, Guixian Zhang, Yanmei Zhang, Rui Bing, Xiao Liu · IEEE Transactions on Computational Social Systems · 2025
The multibehavior recommendation aims at alleviating the data sparsity problem and improving recommendation accuracy by exploiting the rich knowledge in auxiliary behaviors. However, existing methods focus on modeling the relationships between behaviors while ignoring users’ interaction intents, making it difficult to capture users’ fine-grained interest requirements, which leads to a decline in user experience. To address this issue, we propose a multibehavior intent disentangled recommendation (MBIDR) model. First, we design an intent-aware interaction classifier that automatically identifies various intents based on user and item characteristics, classifying interactions into different categories to better explore users’ fine-grained interests. Second, we develop an adaptive relation learning approach that enables the model to better capture the varying importance of different interaction patterns in relation to user preferences. Third, we introduce multitask learning and nonsampling loss, which effectively leverage richer supervisory signals to enhance model training performance. Finally, extensive experiments on two real datasets demonstrate the effectiveness of MBIDR over baselines, with the best improvement reaching 16.20%.