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Scientific Programming
Volume 2017, Article ID 4379141, 9 pages
Research Article

A Novel Hybrid Similarity Calculation Model

1School of Information Science and Engineering, Central South University, Hunan, China
2Hunan University of Finance and Economics, Hunan, China
3School of Software, Central South University, Hunan, China

Correspondence should be addressed to Zhifang Liao; nc.ude.usc@oailfz

Received 25 August 2017; Accepted 12 November 2017; Published 4 December 2017

Academic Editor: Longxiang Gao

Copyright © 2017 Xiaoping Fan 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.


This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper. This model consists of two parts, on the one hand the model uses the function fitting to describe users’ rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation. Experimental results show that, for data sets of different size, the model’s prediction precision is higher than the traditional recommendation algorithms.