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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tran Vu Pham Le Nguyen Thach |
| Copyright Year | 2011 |
| Abstract | In recent years, huge amount of digital documents have been created in online social communities. Locating and recommending relevant documents to users in need is not a trivial task. In this paper, we introduce an approach to computing document similarity for building document recommender systems. This approach takes into account document internal content, together with social tags and associated users. These three factors are considered as three dimensions of a document in social space. The similarity computation is performed independently in each dimension, and then aggregated according to user preferences. This allows users to customize their views over the document. Experiments have been conducted with the proposed approach using data crawled from Cite Like. Experimental result has confirmed that all three dimensions have important contributions to document similarity computation. |
| Starting Page | 872 |
| Ending Page | 878 |
| File Size | 812520 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467300063 |
| DOI | 10.1109/DASC.2011.147 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-12 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | recommender system Navigation Computational modeling Communities Semantics Collaboration information retrieval Tagging social computing Recommender systems document matching |
| Content Type | Text |
| Resource Type | Article |
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