Personalized information retrieval in digital ecosystems
Search results personalization is considered a promising approach to boost the quality of text retrieval. In this paper, a personalized information retrieval paradigm is proposed which not only implicitly creates user profile by learning users? search history, search preferences, and desktop informa...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
IEEE
2008
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/23993 |
| _version_ | 1848751305686777856 |
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| author | Zhu, Dengya Dreher, Heinz |
| author_facet | Zhu, Dengya Dreher, Heinz |
| author_sort | Zhu, Dengya |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Search results personalization is considered a promising approach to boost the quality of text retrieval. In this paper, a personalized information retrieval paradigm is proposed which not only implicitly creates user profile by learning users? search history, search preferences, and desktop information by kNN algorithm; but also intends to deal with the problem of search concepts drift through adjusting theweight of category which represents users? search preference.By comparing the cosine similarities between vectors represent personal valued search concepts in user profiles, and vectors represent search concepts in the retrieved search results, the search results will be tailed to better match users? information needs. |
| first_indexed | 2025-11-14T07:50:37Z |
| format | Conference Paper |
| id | curtin-20.500.11937-23993 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:50:37Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-239932017-09-13T13:52:24Z Personalized information retrieval in digital ecosystems Zhu, Dengya Dreher, Heinz kNN user profile personalization information retrieval machine learning Search results personalization is considered a promising approach to boost the quality of text retrieval. In this paper, a personalized information retrieval paradigm is proposed which not only implicitly creates user profile by learning users? search history, search preferences, and desktop information by kNN algorithm; but also intends to deal with the problem of search concepts drift through adjusting theweight of category which represents users? search preference.By comparing the cosine similarities between vectors represent personal valued search concepts in user profiles, and vectors represent search concepts in the retrieved search results, the search results will be tailed to better match users? information needs. 2008 Conference Paper http://hdl.handle.net/20.500.11937/23993 10.1109/DEST.2008.4635207 IEEE fulltext |
| spellingShingle | kNN user profile personalization information retrieval machine learning Zhu, Dengya Dreher, Heinz Personalized information retrieval in digital ecosystems |
| title | Personalized information retrieval in digital ecosystems |
| title_full | Personalized information retrieval in digital ecosystems |
| title_fullStr | Personalized information retrieval in digital ecosystems |
| title_full_unstemmed | Personalized information retrieval in digital ecosystems |
| title_short | Personalized information retrieval in digital ecosystems |
| title_sort | personalized information retrieval in digital ecosystems |
| topic | kNN user profile personalization information retrieval machine learning |
| url | http://hdl.handle.net/20.500.11937/23993 |