A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems

Abstract

Recommender system has become one of the most popular techniques to cope with the information overload problem. In the past years many algorithms have been proposed to obtain accurate recommendations. Such methods usually put all the collected user data into learning models without a careful consideration of the quantity and quality of individual user feedbacks. Yet in real applications, different types of users tend to represent preferences and opinions in various ways, thus resulting in user data with radically diverse quantity and quality. This characteristic of data influences the performance of recommendations. However, little attention has been devoted to the management of quantity and quality for user data in recommender systems. In this paper, we propose a generic framework to seamlessly exploit different pre-processing and recommendation approaches for ratings of different users. More specifically, we first classify users into groups based on the quantity and quality of their behavior data. In order to handle the user groups diversely, we further propose several data pre-processing strategies. Subsequently, we present a novel transfer latent factor model (TLMF) to transfer learnt models between groups. Finally, we conduct extensive experiments on a large data set and demonstrate the effectiveness of our proposed framework.