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Intelligent Information Systems, IASTED International Conference on (2009)
Haikou, China
Apr. 24, 2009 to Apr. 25, 2009
ISBN: 978-0-7695-3618-7
pp: 152-154
Predicting products a customer would like on the basis of other customers’ ratings for these products has become a well known approach adopted by many personalized recommendation systems on the Internet. With the development of electronic commerce, the number of customers and products grows rapidly, resulted in the sparsity of the rating dataset. Poor quality is one major challenge in collaborative filtering recommender systems. To solve this problem, the paper proposed a personalized recommendation algorithm combining slope one scheme and user based collaborative filtering. This method employs slope one scheme technology to fill the vacant ratings of the user-item matrix where necessary. Then it utilizes the user based collaborative filtering to produce the recommendation. The experiments were made on a common data set using different filtering algorithms. The results show that the proposed recommender algorithm combining slope one scheme and user based collaborative filtering can improve the accuracy of the collaborative filtering recommender system.
recommendation algorithm, collaborative filtering, slope one scheme, sparsity

H. Ye and P. Wang, "A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering," 2009 International Conference on Industrial and Information Systems (IIS 2009)(IIS), Haikou, 2009, pp. 152-154.
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