{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/topicvae-topic-aware-disentanglement","title":"TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced Recommendation","arxiv_id":null,"date":"2022-10-10","proceeding":"ACM MM 2022 10","authors":["Zhiqiang Guo","GuoHui Li","Jianjun Li","Huaicong Chen"],"abstract":"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 novel Topic-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.","url_abs":"https://doi.org/10.1145/3503161.3548294","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3503161.3548294","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"topicvae-topic-aware-disentanglement","repo_url":"https://github.com/georgeguo-cn/TopicVAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"disentanglement","task_name":"Disentanglement"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"beta-vae","method_name":"Beta-VAE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}