Multi-representation Variational Autoencoder via iterative latent attention and implicit differentiation

• Main Authors: TRAN, Nhu Thuat, LAUW, Hady Wirawan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Information systems; Information retrieval; Recommender systems; Variational Autoencoder; Applied Statistics; Artificial Intelligence and Robotics; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/8350; https://ink.library.smu.edu.sg/context/sis_research/article/9353/viewcontent/cikm23.pdf
• Institution: Singapore Management University

Variational Autoencoder (VAE) offers a non-linear probabilistic modeling of user's preferences. While it has achieved remarkable performance at collaborative filtering, it typically samples a single vector for representing user's preferences, which may be insufficient to capture the user's diverse interests. Existing solutions extend VAE to model multiple interests of users by resorting a variant of self-attentive method, i.e., employing prototypes to group items into clusters, each capturing one topic of user's interests. Despite showing improvements, the current design could be more effective since prototypes are randomly initialized and shared across users, resulting in uninformative and non-personalized clusters.To fill the gap, firstly, we introduce iterative latent attention for personalized item grouping into VAE framework to infer multiple interests of users. Secondly, we propose to incorporate implicit differentiation to improve training of our iterative refinement model. Thirdly, we study the self-attention to refine cluster prototypes for item grouping, which is largely ignored by existing works. Extensive experiments on three real-world datasets demonstrate stronger performance of our method over those of baselines.librar