Integrating textual reviews into neighbor-based recommender systems

Abstract

Recommender systems are developed to personalize services for each user. The focus of recommender systems is to accurately discover the unknown preferences of users. To address this issue, neighbor-based recommender systems compute a weighted average of neighbors’ known preferences to predict the unknown preferences of an active user. Its weights represent the influence of neighbors’ preferences on the active user’s ones. Previous research estimates these weights solely based on users’ observed preferences. However, preferences are commonly expressed through numerical ratings, presenting a challenge for users to grasp the rating scale and assign the most precise numerical values. Therefore, the observed rating data is characterized by sparsity, a lack of precision, and insufficient detail. In modern recommender systems, besides collecting ratings, it’s entirely possible to gather various types of information. One valuable data source proven to enhance recommender systems significantly is textual reviews authored by users following their experiences with items. In this study, we utilize Bert models to derive user vectors from observed textual reviews. These vectors are then employed to estimate weights between the active user and each neighbor in a neighbor-based recommender system. In contrast to numerical ratings, textual reviews provide a more detailed and precise representation of users’ preferences. This contributes to improving the performance of neighbor-based recommender systems.