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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li Qiang Chen Qin Wang Rong-bo |
| Copyright Year | 2009 |
| Abstract | To improve search precision, a model of personalized meta-search engine is presented. In this model, a query result ranking algorithm called Weighted-Position & Abstract Ranking based-on User Profile (WPARUP) is proposed, which integrates these threes factors: global similarity between query result's abstract and the query, the position information of query results, the relevance between member search engine and the user’s search topic. The results illustrate that the ranking algorithm can perfectly rank high relevancy results on query in the front of the list, thus improve the search precision. |
| Starting Page | 21 |
| Ending Page | 25 |
| File Size | 595526 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769535708 |
| DOI | 10.1109/WCSE.2009.347 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-05-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Merging Software algorithms Ontologies Educational institutions Result ranking Meta-search engine Learning systems User profile model Feedback Weighted-position & abstract ranking Machine learning Search engines Metasearch Software engineering |
| Content Type | Text |
| Resource Type | Article |
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