Abstract
This paper proposes a novel approach to automatic tag recommendation for weblogs/blogs. It makes use of collective intelligence extracted from Web 2.0 collaborative tagging as well as word semantics to learn how to predict the best set of tags to use, using a Vector Space Model (VSM), comparing similar blogs from the web and statistical methods. Tags are popular means of annotating and organizing content on the web, from photos, videos and music to blogs. Unfortunately, tagging is just a manual process and limited to the users’ own knowledge and experience. There may be more accurate or popular tags to describe the same content. Collaborative tagging is a recent technology that creates collective intelligence by observing how different users tag similar content. Our research makes use of this collective intelligence to automatically generate tag suggestions to blog authors based on the semantic content of blog entries.
| Original language | English |
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| Publication status | Published - 6 Apr 2008 |
| Event | 7th WSEAS International Conference on Applied Computer and Applied Computation Science (ACACOS'08) - Hangzhou, China Duration: 6 Apr 2008 → 8 Apr 2008 |
Conference
| Conference | 7th WSEAS International Conference on Applied Computer and Applied Computation Science (ACACOS'08) |
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| Country/Territory | China |
| City | Hangzhou |
| Period | 6/04/08 → 8/04/08 |