Identification of grey sheep users by histogram intersection in recommender systems

Abstract

Collaborative filtering, as one of the most popular recommendation algorithms, has been well developed in the area of recommender systems. However, one of the classical challenges in collaborative filtering, the problem of “Grey Sheep” user, is still under investigation. “Grey Sheep” users is a group of the users who may neither agree nor disagree with the majority of the users. They may introduce difficulties to produce accurate collaborative recommendations. In this paper, discuss the drawbacks in the approach that can identify the Grey Sheep users by reusing the outlier detection techniques based on the distribution of user-user similarities. We propose to alleviate these drawbacks and improve the identification of Grey Sheep users by using histogram intersection to better produce the user-user similarities. Our experimental results based on the MovieLens 100 K rating data demonstrate the ease and effectiveness of our proposed approach in comparison with existing approaches to identify grey sheep users.