Expert-opinions Based Linear Regression Model for Top-N Recommendation

Abstract

Expert-opinions approaches based on CF (collaborative filtering) have demonstrated a successful means for top-N recommendation, such as Expert-CF. However, the choice of the similarity measure used for evaluation of user-expert relationships is crucial for the success of such approaches. In this paper, we present an approach to calculate user-expert similarities by formulating a regression problem which enables us to extract the similarities from the data in a problem specific way. A comprehensive set of experiments is conducted by predicting a subset of the MovieLens data set. We use ratings crawled from a web portal of expert reviews to generate high quality recommendations. The experiments show that the proposed method improves upon the standard Expert-CF model and outperforms other state-of-the-art top-N recommendation approaches in terms of achieving good balance between recommendation quality and speed.