Interest-Disentangled Contrastive Sample Generation for Recommendation

In the domain of recommendations, previous works often retrieve items through sampling strategies from the database to gather negative signals for exploring implicit feedback. However, because of extremely sparse records, the existing items used as negative samples may not sufficiently support the interacted items in depicting the diverse interests of users. Consequently, the generation of negative samples needs to be explored in recommendation systems. In this study, we propose an interest-disentangled contrastive sample generation (IDCG) model to enhance interest modeling by contrasting interacted items with the generated samples for recommendation. Specifically, we decouple the interacted items of users into positively relevant and irrelevant factors of interest, providing a valuable clue to learn negatively relevant factors in personalized interests. Then, negative samples are generated by merging the learned negatively relevant factors and irrelevant factors. At this point, a two-level contrast is constructed between positive and negative samples and between the relevant factors of positives and negatives, providing auxiliary collaborative signals to debias and alleviate the interaction sparsity issue. Extensive experiments on three real datasets demonstrate the effectiveness of IDCG in generating targeted and meaningful negative samples from the perspective of disentangling relevant factors to promote interest modeling for recommendation.