Translation-based Embedding Model for Rating Conversion in Recommender Systems

Abstract

Ratings, which are explicit feedback, are the most popular form that is often used in Recommender System (RSs). However, using the actual ratings from neighbors to predict ratings of target user toward target item often leads to low accuracy prediction due to the improper rating range problem. Rating conversion methods are proposed to solve this problem over the past few years. To propose rating conversion method, each user’s preference or rating pattern is needed. Some studies adopt the idea from translation-based embedding model and represent user’s preference in graph form. Although some studies represent users, items, and relations in embedding vector form, their representation may be improper and inaccurate if the rating pattern of each user is not in the same range. These vectors still suffer from the improper rating range as well. In this work, we propose a translation-based embedding model with rating conversion in RSs. We aim to solve the improper rating range problem in translation-based embedding model. Our challenges are 1) representing the relation (rating) between a pair of user and item in vector form, instead of scalar form and 2) dealing with rating conversion of user’s rating in vector form. The FilmTrust and MovieLens dataset are used in experiments comparing the proposed method with the existing methods. The evaluation showed that the proposed rating conversion method provides better accuracy results in term of both rating prediction and ranking recommendation.