Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
The Collaborative Filtering Recommendation Based on Similar-Priority and Fuzzy Clustering
PEITS '08 Proceedings of the 2008 Workshop on Power Electronics and Intelligent Transportation System
Combining Singular Value Decomposition and Item-based Recommender in Collaborative Filtering
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
Classification-based collaborative filtering using market basket data
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Recommender system is one of the most important technologies in electronic commerce. And the collaborative filtering is almost the popular approach used in the recommender systems. With the development of electronic commerce systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Sparsity of users' ratings is the major reason causing the poor quality. To address this issue, a collaborative filtering recommendation algorithm based on singular value decomposition (SVD) smoothing is presented. This approach predicts item ratings that users have not rated by the employ of SVD technology, and then uses Pearson correlation similarity measurement to find the target users' neighbors, lastly produces the recommendations. The collaborative filtering recommendation algorithm based on SVD smoothing can alleviate the sparsity problems of the user item rating dataset, and can provide better recommendation than traditional collaborative filtering algorithms.