Learning multi-faceted prototypical user interests

• Main Authors: TRAN, Nhu Thuat, LAUW, Hady W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: multi-faceted representation; user interests; item characteristics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9281; https://ink.library.smu.edu.sg/context/sis_research/article/10281/viewcontent/5185_Learning_Multi_Faceted_Pr.pdf
• Institution: Singapore Management University

We seek to uncover the latent interest units from behavioral data to better learn user preferences under the VAE framework. Existing practices tend to ignore the multiple facets of item characteristics, which may not capture it at appropriate granularity. Moreover, current studies equate the granularity of item space to that of user interests, which we postulate is not ideal as user interests would likely map to a small subset of item space. In addition, the compositionality of user interests has received inadequate attention, preventing the modeling of interactions between explanatory factors driving a user's decision. To resolve this, we propose to align user interests with multi-faceted item characteristics. First, we involve prototype-based representation learning to discover item characteristics along multiple facets. Second, we compose user interests from uncovered item characteristics via binding mechanism, separating the granularity of user preferences from that of item space. Third, we design a dedicated bi-directional binding block, aiding the derivation of compositional user interests. On real-world datasets, the experimental results demonstrate the strong performance of our proposed method compared to a series of baselines.