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Applying Collaborative Filtering Techniques for Individual Fashion ecommendation
Lei Jianlan,Wang Jin *,Lu Guodong
State Key Laboratory of CAD & CG,Zhejiang University,Hangzhou 310027,China
*Correspondence author
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Funding: 教育部博士点基金,863,中国博士后基金（No.20060335118,2009AA04Z103,20090451449）
Opened online:28 December 2009
Accepted by: none
Citation: Lei Jianlan,Wang Jin,Lu Guodong.Applying Collaborative Filtering Techniques for Individual Fashion ecommendation[OL]. [28 December 2009] http://en.paper.edu.cn/en_releasepaper/content/38147
 
 
Collaborative filtering (CF) technique is the most successful method for recommendation system. In this article, we developed a fashion recommendation system by using CF technique. In order to improve on data sparseness problems in CF technique, firstly we built users’ similarities based on users’ background information which is related with fashion, then the neighbors’ predicting ratings were filled into the U-I rating matrix in advance before the traditional collaborative filtering. While computing the background information similarities, we develop a hybrid similarity model which can deal with different types of properties. The method can solve the data sparseness of U-I rating matrix effectively.
Keywords:collaborative filtering;similarity computing;clothes recommendation
 
 
 

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