Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System
Year: 2023
Type: preprint
Abstract: A conversational recommender system (CRS) is a practical application for item recommendation through natural language conversation. Such a system estimates user interests for appropriate personalized recommendations. Users sometimes have various interests in different categories or genres, but existing studies assume a unique user interest that can be covered by closely related items. In this work, we propose to model such multiple user interests in CRS. We investigated its effects in experiments using the ReDial dataset and found that the proposed method can recommend a wider variety of items than that of the baseline CR-Walker.
Source: arXiv (Cornell University)
Authors: Okuda, Yuka, Sudoh, Katsuhito, Shinagawa, Seitaro, Nakamura, Satoshi
Institution: Nara Institute of Science and Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4322832397