Aligning dual disentangled user representations from ratings and textual content

• Main Authors: TRAN, Nhu Thuat, LAUW, Hady Wirawan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: disentangled representation; textual content-aware recommender systems; user preferences interpretation; Numerical Analysis and Scientific Computing; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7598; https://ink.library.smu.edu.sg/context/sis_research/article/8601/viewcontent/kdd22b.pdf
• Institution: Singapore Management University

Classical recommendation methods typically render user representation as a single vector in latent space. Oftentimes, a user's interactions with items are influenced by several hidden factors. To better uncover these hidden factors, we seek disentangled representations. Existing disentanglement methods for recommendations are mainly concerned with user-item interactions alone. To further improve not only the effectiveness of recommendations but also the interpretability of the representations, we propose to learn a second set of disentangled user representations from textual content and to align the two sets of representations with one another. The purpose of this coupling is two-fold. For one benefit, we leverage textual content to resolve sparsity of user-item interactions, leading to higher recommendation accuracy. For another benefit, by regularizing factors learned from user-item interactions with factors learned from textual content, we map uninterpretable dimensions from user representation into words. An attention-based alignment is introduced to align and enrich hidden factors representations. A series of experiments conducted on four real-world datasets show the efficacy of our methods in improving recommendation quality.