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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
Wang Daoping, Jiang Zhongyang(), Zhang Boqing
Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
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Abstract  

[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
ZTFLH:  F270 G35  

Cite this article:

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

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0017     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I6/102

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