Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System

Recommender System is an attractive field of research and development for many commercial applications. A typical recommender system recommends items to users using collaborative filtering [1, 2] based on a large amount of accumulated data from other users' choices. A major drawback of this approach is the so-called cold start problem [3]; when a target user has no history in order to identify his/her interests and preferences for the recommendation. Interaction with users can mitigate this problem by iteratively updating their interests and preferences. Natural language conversation is a promising way for interaction between users and recommender systems, especially for new under-experienced users. Conversational Recommender System (CRS) [4, 5] is a variant of such a recommender system. CRS recommends items to users according to their user portrait through conversation. The user portrait is a representation of user interests used for the recommendation [6]. Existing CRS studies [5, 6] represent a user portrait using a userdependent embedding vector and use it to choose appropriate items for recommen-Yuka Okuda Nara Institute of Science and Technology, Ikoma, Nara, Japan, e-mail: okuda.yuka.ou0@is.