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...