A Collaborative Filtering Recommendation Algorithm based on Cluster Analysis
AUTHORS
Matteo Scarpa, University of Pisa, Pisa, Italy
Felice Antonio Caserta, University of Pisa, Pisa, Italy
ABSTRACT
As the application of personalized recommendation systems in e-commerce websites becomes more and more extensive, the research on personalized recommendation algorithms is constantly deepening. The personalized recommendation system has brought huge commercial benefits to the e-commerce field. Based on the item-based top-N collaborative filtering recommendation algorithm, this paper proposes a top-N collaborative filtering recommendation algorithm based on the K-means clustering algorithm. Using K-means to cluster according to the distance between the sample points, the similarity between users is regarded as the distance, the users are clustered into several clusters, and then the recommendation algorithm is applied in each cluster to perform recommendations. This paper introduces factors such as item time difference, popular item weight, and user common rating weight into the similarity measurement formula. The experimental results show that the recall rate of the algorithm proposed in this paper is higher than that of the traditional collaborative filtering recommendation algorithm2. 1%, proved the correctness of the proposed algorithm.
KEYWORDS
Cluster analysis, Similarity calculation, Collaborative filtering algorithm, Item similarity, Threshold, Recall rate
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