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Application of Improved K‐Means Algorithm in Collaborative Recommendation System

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  • Hui Jing

Abstract

With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information filtering and information refinement. However, the recommendation quality and efficiency of collaborative filtering recommendation algorithms are low. The research combines the improved artificial bee colony algorithm with K‐means algorithm and applies them to the recommendation system to form a collaborative filtering recommendation algorithm. The experimental results show that the MAE value of the new fitness function is 0.767 on average, which has good separation and compactness in clustering effect. It shows high search accuracy and speed. Compared with the original collaborative filtering algorithm, the average absolute error value of this algorithm is low, and the running time is only 50 s. It improves the recommendation quality and ensures the recommendation efficiency, providing a new research path for the improvement of collaborative filtering recommendation algorithm.

Suggested Citation

  • Hui Jing, 2022. "Application of Improved K‐Means Algorithm in Collaborative Recommendation System," Journal of Applied Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jnljam:v:2022:y:2022:i:1:n:2213173
    DOI: 10.1155/2022/2213173
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    References listed on IDEAS

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    1. Xibin Wang & Zhenyu Dai & Hui Li & Jianfeng Yang & Wei Wang, 2021. "Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis," Complexity, Hindawi, vol. 2021, pages 1-11, March.
    2. Jun Zhu & Lixin Han & Zhinan Gou & Xiaofeng Yuan, 2018. "A fuzzy clustering‐based denoising model for evaluating uncertainty in collaborative filtering recommender systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(9), pages 1109-1121, September.
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