A recommendation algorithm combining clustering method and slope one scheme
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Building user profiles to improve user experience in recommender systems
Proceedings of the sixth ACM international conference on Web search and data mining
Weighted slope one predictors revisited
Proceedings of the 22nd international conference on World Wide Web companion
TV predictor: personalized program recommendations to be displayed on SmartTVs
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
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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.