Open Access

ARTICLE

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Weijin Jiang^1,2,3, Jiahui Chen^1,*, Yirong Jiang^4,*, Yuhui Xu¹, Yang Wang¹, Lina Tan¹, Guo Liang⁵

1 Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University of Technology and Business, Changsha, 410205, China.
2 Institute of Big Data and Internet Innovation, Hunan University of Technology and Business, Changsha, 410205, China.
3 School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430073, China.
4 Tonghua Normal University, Tonghua, 134002, China.
5 School of Bioinformatics, University of Minnesota, Twin Cities, USA.

*Corresponding Authors: Chen Jiahui. Email: '" />. Yirong Jiang. Email: '" />.

Computers, Materials & Continua 2019, 61(2), 849-859. https://doi.org/10.32604/cmc.2019.05932

Abstract

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation, so that the newer evaluation information in the system has a relatively large weight. The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.

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Citations