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New Technology of Library and Information Service  2015, Vol. 31 Issue (6): 13-19    DOI: 10.11925/infotech.1003-3513.2015.06.03
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Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification
Zhu Ting, Qin Chunxiu, Li Zuhai
School of Economics and Management, Xidian University, Xi'an 710071, China
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[Objective] To solve the problem of low efficiency of the algorithm with the increasing number of users. [Methods] This paper proposes a method of collaborative filtering based on user classification. Firstly, the huge users are classified into several groups according to a rule-based classification method. Then, with the guarantee of recommendation accuracy, the local neighbor users are discovered for users. Finally, based on the discovered local neighbors, personalized recommendation is conducted. [Results] User classification and recommendation accuracy are evaluated by F1 and MAE separately. The algorithm efficiency is evaluated according to the time complexity. Experimental results show that with the adoption of a rule-based user classification, collaborative filtering algorithm significantly improves with the guarantee of user classification accuracy and recommendation accuracy. [Limitations] The recommendation accuracy is reduced a little bit. The proposed method is only tested on MovieLens data set, and it needs further validation in other data sets. [Conclusions] This method reduces the computation of local neighbors user identification, while improves the efficiency of the algorithm.

Key wordsPersonalized recommendation      Collaborative filtering      User classification      Rule     
Received: 31 December 2014      Published: 08 July 2015
:  G350  

Cite this article:

Zhu Ting, Qin Chunxiu, Li Zuhai. Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification. New Technology of Library and Information Service, 2015, 31(6): 13-19.

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