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International Journal of Digital Multimedia Broadcasting
Volume 2017, Article ID 1386461, 7 pages
https://doi.org/10.1155/2017/1386461
Research Article

A Novel Preferential Diffusion Recommendation Algorithm Based on User’s Nearest Neighbors

1School of Information Technology, Jiangxi University of Finance & Economics, Nanchang 330013, China
2Research Institution for Information Resource Management, Jiangxi University of Finance & Economics, Nanchang 330013, China

Correspondence should be addressed to Fuguo Zhang; moc.361@liam_dribder

Received 1 March 2017; Accepted 11 April 2017; Published 4 May 2017

Academic Editor: Hyo-Jong Lee

Copyright © 2017 Fuguo Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recommender system is a very efficient way to deal with the problem of information overload for online users. In recent years, network based recommendation algorithms have demonstrated much better performance than the standard collaborative filtering methods. However, most of network based algorithms do not give a high enough weight to the influence of the target user’s nearest neighbors in the resource diffusion process, while a user or an object with high degree will obtain larger influence in the standard mass diffusion algorithm. In this paper, we propose a novel preferential diffusion recommendation algorithm considering the significance of the target user’s nearest neighbors and evaluate it in the three real-world data sets: MovieLens 100k, MovieLens 1M, and Epinions. Experiments results demonstrate that the novel preferential diffusion recommendation algorithm based on user’s nearest neighbors can significantly improve the recommendation accuracy and diversity.