论文信息 - A Hybrid Collaborative Filtering Algorithm Based on User-Item

A Hybrid Collaborative Filtering Algorithm Based on User-Item

Collaborative filtering is one of the most important technologies in e-commerce recommendation system. Traditional similarity measure methods work poorly when the user rating data are extremely sparse. Aiming at this issue a hybrid collaborative filtering is proposed. This method used a novel similarity measure method to predict the target item rating and it fused the advantages of the user-based algorithm and item-based algorithm with the control factor α. The experimental results show that this improved algorithm obviously enhances the recommended accuracy, and provide better recommendation quality.

Min Yu | Yan-ni Chen

[1] John Riedl,et al. Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[2] John Riedl,et al. Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[3] Hyung Jun Ahn,et al. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[4] Zeng Chun,et al. A Survey of Personalization Technology , 2002 .

[5] Deng Ai,et al. A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction , 2003 .

[6] Li Peng. Survey of Collaborative Filtering Algorithms , 2009 .

[7] Douglas B. Terry,et al. Using collaborative filtering to weave an information tapestry , 1992, CACM.

[8] Yoon Ho Cho,et al. Mining changes in customer buying behavior for collaborative recommendations , 2005, Expert Syst. Appl..

[9] Sooyoung Kim,et al. Classification-based collaborative filtering using market basket data , 2005, Expert Syst. Appl..

[10] Philip S. Yu,et al. Horting hatches an egg: a new graph-theoretic approach to collaborative filtering , 1999, KDD '99.

[11] Jian Zhu,et al. Collaborative Filtering Recommendation Algorithm Based on Item Clustering and Global Similarity , 2012, 2012 Fifth International Conference on Business Intelligence and Financial Engineering.