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New Technology of Library and Information Service  2015, Vol. 31 Issue (12): 28-33    DOI: 10.11925/infotech.1003-3513.2015.12.05
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Research on Multi-granularity Users' Preference Mining Based on Collaborative Filtering Personalized Recommendation
Song Meiqing
School of Information Management, Wuhan University, Wuhan 430072, China
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[Objective] Researching the relationship between users' preference mining granularity and mining efficiency in collaborative filtering, this paper aims at finding out the most efficient mining granularity. [Methods] According to the practical application, the users' preference mining granularity is divided into three kinds from coarse-grained to fine-grained, and then design the corresponding preference mining algorithm under the three kinds of granularities, finally contrast users' preference mining efficiency under different granularities through experiments. [Results] Experimental results show that the preference mining efficiency reduces as the users' preference mining granularity changes from coarse to fine. [Limitations] Data only includes users' consumption data and rating data, other types of data are not covered temporarily. [Conclusions] Coarse-grained preference mining is better for discovering users' preferences.

Received: 05 June 2015      Published: 06 April 2016
:  G202  

Cite this article:

Song Meiqing. Research on Multi-granularity Users' Preference Mining Based on Collaborative Filtering Personalized Recommendation. New Technology of Library and Information Service, 2015, 31(12): 28-33.

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