Dynamic Popularity-Aware Contrastive Learning for Recommendation

With the development of deep learning techniques, contrastive representation learning has been increasingly employed in large-scale recommender systems. For instance, deep user-item matching models can be trained by contrasting positive and negative examples and learning discriminative user and item representations. Despite their success, the distinguishable properties of the recommender system are often ignored in existing modelling. Standard methods approximate maximum likelihood estimation on user behavior data in a manner similar to language models. Specifically, the way of model optimization corresponds to approximating the user-item pointwise mutual information, which can be regarded as eliminating the influence of global item popularity on user behavior to capture intrinsic user preference. In addition, unlike the situation in language models where word frequency is relatively stable, item popularity is constantly evolving. To address these issues, we propose a novel dynamic popularity-aware (DPA) contrastive learning method for recommendation, which consists of two key components: i) a dynamic negative sampling strategy is involved to enhance the user representation, ii) a dynamic prediction recovery is adopted by the real-time item popularity. The proposed strategy can be naturally overlaid on any contrastive learning-based matching model to more accurately capture user interest and system dynamics. Finally, the effectiveness of the proposed strategy is demonstrated through comprehensive experiments on an e-commerce scenario of Alibaba Group.