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A Proposal on Social Tagging Systems Using Tensor Reduction and Controlling Requests
| Content Provider | Semantic Scholar |
|---|---|
| Author | Manopriya, M. |
| Copyright Year | 2012 |
| Abstract | Social Tagging System is the process in which user makes their interest by tagging on a particular item. These STS are in associated with web 2.0 and has sourceful information for the users with their recommendations. It provides different types of recommendations are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the KernelSVD smoothing technique. We provide now with the 4-order tensor approach, which we named as Tensor Reduction. Here the items that are tagged can be viewed by the user who are recommended the same item and tagged over it. There by can improve the social tagging recommendations efficiency and also the unwanted request has been controlled. The results show significant improvements in terms of effectiveness. |
| Starting Page | 49 |
| Ending Page | 57 |
| Page Count | 9 |
| File Format | PDF HTM / HTML |
| DOI | 10.5121/ijcsa.2012.2105 |
| Volume Number | 2 |
| Alternate Webpage(s) | http://wireilla.com/papers/ijcsa/V2N1/2112ijcsa05.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
