Improving web search by categorization, clustering, and personalization
This research combines Web snippet1 categorization, clustering and personalization techniques to recommend relevant results to users. RIB - Recommender Intelligent Browser which categorizes Web snippets using socially constructed Web directory such as the Open Directory Project (ODP) is to bedevelop...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Springer
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/43954 |
| _version_ | 1848756858258456576 |
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| author | Zhu, Dengya Dreher, Heinz |
| author2 | CH Tang |
| author_facet | CH Tang Zhu, Dengya Dreher, Heinz |
| author_sort | Zhu, Dengya |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This research combines Web snippet1 categorization, clustering and personalization techniques to recommend relevant results to users. RIB - Recommender Intelligent Browser which categorizes Web snippets using socially constructed Web directory such as the Open Directory Project (ODP) is to bedeveloped. By comparing the similarities between the semantics of each ODP category represented by the category-documents and the Web snippets, the Web snippets are organized into a hierarchy. Meanwhile, the Web snippets are clustered to boost the quality of the categorization. Based on an automatically formed user profile which takes into consideration desktop computer informationand concept drift, the proposed search strategy recommends relevant search results to users. This research also intends to verify text categorization, clustering, and feature selection algorithms in the context where only Web snippets are available. |
| first_indexed | 2025-11-14T09:18:52Z |
| format | Conference Paper |
| id | curtin-20.500.11937-43954 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:18:52Z |
| publishDate | 2008 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-439542022-12-07T06:50:50Z Improving web search by categorization, clustering, and personalization Zhu, Dengya Dreher, Heinz CH Tang CHX Ling X Zhou NJ Cercone X Li Web snippets Web searching personalization text categorization clustering This research combines Web snippet1 categorization, clustering and personalization techniques to recommend relevant results to users. RIB - Recommender Intelligent Browser which categorizes Web snippets using socially constructed Web directory such as the Open Directory Project (ODP) is to bedeveloped. By comparing the similarities between the semantics of each ODP category represented by the category-documents and the Web snippets, the Web snippets are organized into a hierarchy. Meanwhile, the Web snippets are clustered to boost the quality of the categorization. Based on an automatically formed user profile which takes into consideration desktop computer informationand concept drift, the proposed search strategy recommends relevant search results to users. This research also intends to verify text categorization, clustering, and feature selection algorithms in the context where only Web snippets are available. 2008 Conference Paper http://hdl.handle.net/20.500.11937/43954 10.1007/978-3-540-88192-6_69 Springer fulltext |
| spellingShingle | Web snippets Web searching personalization text categorization clustering Zhu, Dengya Dreher, Heinz Improving web search by categorization, clustering, and personalization |
| title | Improving web search by categorization, clustering, and personalization |
| title_full | Improving web search by categorization, clustering, and personalization |
| title_fullStr | Improving web search by categorization, clustering, and personalization |
| title_full_unstemmed | Improving web search by categorization, clustering, and personalization |
| title_short | Improving web search by categorization, clustering, and personalization |
| title_sort | improving web search by categorization, clustering, and personalization |
| topic | Web snippets Web searching personalization text categorization clustering |
| url | http://hdl.handle.net/20.500.11937/43954 |