Title: A hybrid recommendation algorithm based on time factor

Authors: Yucai Zhou; Tong Wang; Xinlin Zhao

Addresses: School of Energy and Power, Changsha University of Science and Technology, No. 178, Chiling Avenue, Changsha, 410076, China ' College of Information and Communication Engineering, Harbin Engineering University, No. 145, Nantong Avenue, Harbin, 150001, China ' College of Information and Communication Engineering, Harbin Engineering University, No. 145, Nantong Avenue, Harbin, 150001, China

Abstract: With the development of social network, helping users find their interesting information become a primary objective for recommend systems. As a most popular recommend algorithm, collaborative filtering recommendation still remains some shortcomings such as data sparseness, cold start and neglecting of variable user interests. With this in mind, a hybrid recommendation algorithm based on time factor is proposed in this paper. A hybrid recommendation model based on time factor aiming to improve the accuracy of user similarity calculations is proposed. This recommendation model includes the user rating, content feature and time factor. Then, the particle swarm optimisation (PSO) algorithm is exploited to optimise the searching space. The experimental results show that the proposed algorithm can effectively improve accuracy while solving data sparseness and cold start. It can be used in the social network and e-commerce.

Keywords: recommendation algorithms; time factor; intelligent algorithms; social networks; recommender systems; user similarity calculations; particle swarm optimisation; PSO; search space optimisation; data sparseness; cold start; e-commerce; electronic commerce.

DOI: 10.1504/IJSN.2015.072438

International Journal of Security and Networks, 2015 Vol.10 No.4, pp.214 - 221

Received: 24 Mar 2015
Accepted: 24 Mar 2015
Published online: 13 Oct 2015 *

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