TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced Recommendation

Learning disentangled representations that reflect user preference based on user behavior (implicit feedback, such as click and purchase) and content information (e.g., plot description, poster) has become a hot research topic in modern recommender systems. However, most existing methods considering content information are not well-designed to disentangle user preference features due to neglecting the diversity of user preference on different semantic topics of items, resulting in sub-optimal performance and low interpretability. To address this problem, we propose a novelTopic-aware Disentangled Variational AutoEncoder (TopicVAE) to learn disentangled representations for enhanced recommendation. Specifically, we first utilize an attention-based topic extraction to extract the topic-level item representations and topic-item probability distribution from item content, and then introduce variational autoencoder to infer topic-level disentangled user representations. To guide the learning of topic-level disentanglement, we present a topic-guided self-supervised contrastive loss to promote the otherness of different topics by introducing a neighborhood-based user representation as guidance. Besides, a heuristic regularization is designed to force each dimension of the disentangled representations to independently reflect a fine-grained factor of a specific topic (e.g., red or blue for color) for feature-level disentanglement. Extensive experimental studies on three public datasets show that TopicVAE significantly outperforms several state-of-the-art baselines. Further empirical experiments also illustrate the interpretability of disentangled representations learned by TopicVAE.