DocumentCode :
3600050
Title :
A Clustering-Based Similarity Measurement for Collaborative Filtering
Author :
Liang Gu ; Peng Yang ; Yongqiang Dong
Author_Institution :
Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China
fYear :
2014
Firstpage :
282
Lastpage :
287
Abstract :
Similarity measurement is a crucial process in collaborative filtering. User similarity is computed solely based on the numerical ratings of users. In this paper, we argue that the social information of users should be also taken into consideration to improve the performance of traditional similarity measurements. To achieve this, we propose a clustering-based similarity measurement approach incorporating user social information. In order to cluster the users effectively, we propose a novel distance metric based on taxonomy tree which can easily process the numerical and categorical information of users. Meanwhile, we also address how to determine the contribution of different types of information in the distance metric. After clustering the users, we introduce the incorporating strategy of our proposed similarity measurement. We perform a series of experiments on a real world dataset and compare the performance of our approach against that of traditional approaches. Experiments demonstrate that the proposed approach considerably outperforms the traditional approaches.
Keywords :
collaborative filtering; pattern clustering; recommender systems; clustering-based similarity measurement; collaborative filtering; user numerical ratings; user similarity; user social information; Accuracy; Collaboration; Computational modeling; Filtering; Measurement; Taxonomy; Vegetation; similarity; clustering; social information; collaborative filtering; recommendation system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Cloud and Big Data (CBD), 2014 Second International Conference on
Print_ISBN :
978-1-4799-8086-4
Type :
conf
DOI :
10.1109/CBD.2014.50
Filename :
7176106
Link To Document :
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