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Service Computing     
Collaborative filtering algorithm based on Logistic function and user clustering
MAO Yi-yu, LIU Jian-xun, HU Rong, TANG Ming-dong
1. Key Lab of Knowledge Processing and Networked Manufacturing, School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; 2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210000, China
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Abstract  A collaborative filtering algorithm based on Logistic function and user clustering was proposed in view of the data sparsity and scalability issues of collaborative filtering. At first, a user’s preference for service keywords was computed, and a user-keyword preference vector was constructed, based on which users were clustered. Then, a user’s interest in service was computed by using a Logistic function. According to the similarity between users’ interest, the nearest neighbors were found in the cluster, which included the target user. At last, the user’s interest in a service was predicted through the neighbors’ interests in the service, and services with high interest prediction were recommended to the user. The experimental results based on real data set show that this algorithm can achieve higher accuracy than traditional collaborative filtering algorithms, and the running time of clustering algorithm is significantly reduced, which effectively improves the real-time performance of recommendation.

Published: 11 June 2017
CLC:  TP 301  
Cite this article:

MAO Yi-yu, LIU Jian-xun, HU Rong, TANG Ming-dong. Collaborative filtering algorithm based on Logistic function and user clustering. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1252-1258.



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