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Collaborative Filtering Recommendation Based on Item Quality and User Ratings |
Fusen Jiao,Shuqing Li( ) |
College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210046, China |
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Abstract [Objective] This paper proposes a modified collaborative filtering algorithm, aiming to improve the results of personalized recommendations. [Methods] First, we evaluated item quality and corrected user ratings based on their previous records. Then, we identified users with similar interests to generate better recommendations. [Results] We tested the new algorithm on MovieLens dataset and found the MAE was 4.7% higher than those of the traditional or other modified methods. [Limitations] The new algorithm does not address the interests drifting issues. [Conclusions] The proposed algorithm could recommend products to consumers more effectively.
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Received: 25 August 2018
Published: 29 September 2019
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Corresponding Authors:
Shuqing Li
E-mail: leeshuqing@163.com
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