Explicit intent enhanced contrastive learning with denoising networks for sequential recommendation

Sequential recommendation aims to accurately predict the users' next preferences, where user interest is influenced by their intent. Utilizing intent contrastive learning, the sequential recommendation has achieved advanced performance. However, most contrastive learning models address the critical issue of data sparsity using data augmentation, which amplifies the noise present in the original sequences, resulting in learning biased user intent distribution functions, and deteriorating the modeling effectiveness of true intent. To address this issue, we propose a model named Explicit Intent Enhanced Contrastive Learning with Denoising Networks for Sequential Recommendation (EICD-Rec). In EICD-Rec, we design a contrastive learning recommender naturally sensitive to users' true intents. The recommender can adaptively filter noise at different frequency scales in sequences in the frequency domain, thus obtaining purer representations of user intents. Moreover, to further enhance the accurate representation of users' true intents, we model explicit intent. Integrating this explicit intent with implicit intent to construct high-quality self-supervision signals and maximize the joint probability distribution between items and explicit intent, thereby enhancing the accuracy of representing users' true intent. Extensive experimental evaluations on three widely used real-world datasets demonstrate the effectiveness and generality of our proposed EICD-Rec model.