A personalized network-based recommendation approach via distinguishing user’s preference

Abstract

With the rapid growth of commerce and development of Internet technology, a large number of user consumption preferences become available for online market intelligence analysis. A critical demand is to reduce the impact of information overload by using recommendation algorithms. In physical dynamics, network-based recommendation algorithms based on mass-diffusion have been popular for its simplicity and efficiency. In this paper, to solve the problem that most network-based recommendation algorithms cannot distinguish how much the user likes collected items and make resource configuration more reasonable, we propose a novel method called biased network-based inference (BNBI). The proposed method treats rating systems and nonrating systems differently and measures user’s preference for items by means of item similarity. The proposed method is evaluated in real datasets (MovieLens and Last.FM) and compared with some existing classic recommendation algorithms. Experimental results show that the proposed method is more effective and it can reduce the impact of item diversity and discover the real interest of users.