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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Suyun Wei Ning Ye Shuo Zhang Xia Huang Jian Zhu |
| Copyright Year | 2012 |
| Abstract | In order to overcome the limitations of data sparsity and inaccurate similarity in personalized recommendation systems, a new collaborative filtering recommendation algorithm by using items categories similarity and interestingness measure is proposed. In this algorithm, first the items categories similarity matrix is constructed by calculating the item-item category distance, and then analyzes the correlation degree of different items by using interestingness measure, last an improved collaborative filtering algorithm is proposed by combining the information of items categories with item-item interestingness and utilizing improved conditional probability method as the standard item-item similarity measure. Experimental results show this algorithm can effectively alleviate the dataset sparsity problem and achieve better prediction accuracy compared to other well-performing collaborative filtering algorithms. |
| Starting Page | 2038 |
| Ending Page | 2041 |
| File Size | 174700 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467307215 |
| e-ISBN | 9780769547190 |
| DOI | 10.1109/CSSS.2012.507 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-08-11 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Item similarity Correlation Collaborative filtering Collaboration Vegetation Item category Filtering algorithms Recommendation systems Prediction algorithms Interesingnesst measure Recommender systems |
| Content Type | Text |
| Resource Type | Article |
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