Enhancing Intent Understanding and Preference Learning for Sequential Recommendation

Sequential recommendation aims to derive insights from user interaction records and make predictions based on relationships between users and items. However, most existing approaches do not effectively integrate user intents and preferences, which limits their capability to capture user behavior patterns. Additionally, these methods often struggle with poor performance in sparse data scenarios. To address these challenges, we proposeEL4SR, a sequential recommendation approach by integrating intent understanding and preference learning.EL4SRsimultaneously learns user intents and preferences through a dual-channel recommendation module, modeling both item and popularity sequences to enable mutual learning that captures the combined effects of intent and preference. Moreover, we enhance intent learning through contrastive learning, improving adaptability in sparse data contexts. We design several augmentation operators to improve the performance and robustness ofEL4SR. Extensive experiments on MovieLens and Amazon demonstrate the performance of our proposed method across various scenarios.