Title :
A new collaborative filtering approach utilizing item’s popularity
Author :
Xia, Weiwei ; He, Liang ; Ren, Lei ; Chen, Meihua ; Gu, Junzhong
Author_Institution :
Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China
Abstract :
Collaborative filtering (CF) is one of the most successful technologies in recommender systems, and widely used in many personalized recommender areas, such as e-commerce, digital library and so on. However, most collaborative filtering algorithms suffer from data sparsity which leads to inaccuracy of recommendation. In this paper, we focus on nearest-neighbor CF algorithms and propose a new collaborative filtering approach. First, we suggest a new missing data making up strategy before user´s similarity computation, which smoothes the sparsity problem. Meanwhile, the notion of item´s popularity weight is defined and introduced into the computation. After then, when facing with new users, we also find a kind way to alleviate the difficulty in recommendation. The experimental results show our proposed approach outperforms the other existing collaborative filtering algorithms. It can efficiently smooth the inaccuracy caused by ratings sparsity, and can work well in generating recommendation for new users.
Keywords :
information filters; collaborative filtering algorithms; data sparsity; digital library; e-commerce; missing data making up strategy; nearest-neighbor CF algorithms; personalized recommender areas; recommender systems; similarity computation; Collaboration; Collaborative work; Computer science; Digital filters; Electronic commerce; Filtering algorithms; Helium; Optical films; Recommender systems; Software libraries; Collaborative Filtering; Item’s Popularity Weight; Recommender System; Sparsity Problem;
Conference_Titel :
Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2629-4
Electronic_ISBN :
978-1-4244-2630-0
DOI :
10.1109/IEEM.2008.4738117