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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (6): 102-109    DOI: 10.11925/infotech.2096-3467.2018.0017
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Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor
Daoping Wang,Zhongyang Jiang(),Boqing Zhang
Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
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[Objective] This paper presents a collaborative filtering algorithm based on gray correlation analysis and time factor, aiming to address the low similarity resolvability and user’s interest drifting issues of the traditional algorithms. [Methods] First, we proposed a new method to calculate user similarity based on gray relational degree. Then, we used the time weight function to improve the Pearson correlation coefficients. Third, we created a hybrid similarity calculation method and made recommendation based on the neighbors of the target user. Finally, we used the MovieLens dataset to examine the new algorithm. [Results] Compared with the traditional collaborative filtering algorithms and those considering gray correlation analysis or time factor alone, the proposed algorithm reduced the mean absolute error (MAE). [Limitations] It takes the proposed algorithm longer time to calculate the hybrid similarity. [Conclusions] The hybrid similarity method improves the accuracy of recommended items for the target users and has a very good commercial promotion prospect.

Key wordsGray Correlation Analysis      Time Factor      Collaborative Filtering      Hybrid Similarity     
Received: 04 January 2018      Published: 11 July 2018

Cite this article:

Daoping Wang,Zhongyang Jiang,Boqing Zhang. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor. Data Analysis and Knowledge Discovery, 2018, 2(6): 102-109.

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