TY - GEN
T1 - Boosting collaborative filtering based on missing data imputation using item's genre information
AU - Xia, Weiwei
AU - He, Liang
AU - Gu, Junzhong
AU - He, Keqin
AU - Ren, Lei
PY - 2009
Y1 - 2009
N2 - Collaborative filtering (CF) is one of the most successful technologies in recommender systems, and widely used in many personalized recommender applications, such as digital library, e-commerce, news sites, and so on. However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation. This paper is with an eye to missing data imputation strategy in nearest-neighbor CF. We propose an effective CF framework based on missing data imputation before conducting CF process, which utilizes item's genre information. In the experimental evaluations, 19 item's genres are employed in the imputation stage. The results show that the proposed approaches effectively alleviate the negative impact of data sparsity, and perform better prediction accuracy than traditional widely-used CF algorithms.
AB - Collaborative filtering (CF) is one of the most successful technologies in recommender systems, and widely used in many personalized recommender applications, such as digital library, e-commerce, news sites, and so on. However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation. This paper is with an eye to missing data imputation strategy in nearest-neighbor CF. We propose an effective CF framework based on missing data imputation before conducting CF process, which utilizes item's genre information. In the experimental evaluations, 19 item's genres are employed in the imputation stage. The results show that the proposed approaches effectively alleviate the negative impact of data sparsity, and perform better prediction accuracy than traditional widely-used CF algorithms.
KW - Collaborative filtering
KW - Missing data imputation
KW - Recommender system
KW - Sparsity problem
UR - https://www.scopus.com/pages/publications/70449120017
U2 - 10.1109/ICCSIT.2009.5234936
DO - 10.1109/ICCSIT.2009.5234936
M3 - 会议稿件
AN - SCOPUS:70449120017
SN - 9781424445196
T3 - Proceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009
SP - 332
EP - 336
BT - Proceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009
T2 - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009
Y2 - 8 August 2009 through 11 August 2009
ER -