Utilizing Textual Reviews for Visualizing and Understanding User Preferences
Dang Pham, Tuan M. V. Le · 2023
Latent factor models are widely used in recommender systems. In these models, users and items are represented as vectors in a joint latent factor space. The inner products of user vectors and item vectors are used to model the user-item interactions (e.g., ratings). A review is often posted by the user to explain the given rating. Therefore, reviews can be used to understand how users rate the items and to interpret the latent dimensions of user and item vectors. In this paper, we propose a probabilistic model that learns latent vectors of users and items in a two- or three-dimensional space for visualization. Our proposed model also extracts review topics and visualizes them in the same visualization space for interpreting the ratings. We model the user-item interactions by using the distances between users and items in the visualization space. Extensive experiments using several real-world datasets demonstrate the effectiveness of our proposed model in recommendation and visualization tasks.