Collaborative filtering recommendation algorithm based on bisecting K-means clustering

Jia Liu; Xin Kang; Shun Nishide; Fuji Ren

doi:10.1117/12.2580026

12 October 2020 Collaborative filtering recommendation algorithm based on bisecting K-means clustering

Jia Liu, Xin Kang, Shun Nishide, Fuji Ren

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740Y (2020) https://doi.org/10.1117/12.2580026
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

The traditional collaborative filtering recommendation algorithm has the problem of data sparsity and expansibility. Aiming at this problem, and improved bisecting k-means collaborative filtering algorithm proposed.The algorithm first removes unrated items in the rating data matrix based on the Weighted Slope One algorithm preprocessing to reduce its sparsity. Then the preprocessed rating data is clustered based on the bisecting K-means algorithm, which reduces the nearest neighbor search space of the target user by assembling similar objects, thereby improving the algorithm’s expansibility. Finally, use the recommendation algorithm to generate the final result.Experimental results show that the improved bisecting k-means algorithm improves the recommendation effect.

Citation Download Citation

Jia Liu, Xin Kang, Shun Nishide, and Fuji Ren "Collaborative filtering recommendation algorithm based on bisecting K-means clustering", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740Y (12 October 2020); https://doi.org/10.1117/12.2580026

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