Evolving information filtering for personalized information service
| Autor: | Dingxing Wang, Fanjiang Tian, Congrong Li |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Service (systems architecture)
Computer science media_common.quotation_subject Information processing Information quality Information needs computer.software_genre Computer Science Applications Theoretical Computer Science Computational Theory and Mathematics Hardware and Architecture Collaborative filtering Quality (business) Relevance (information retrieval) Data mining computer Software Information filtering system media_common |
| Zdroj: | Journal of Computer Science and Technology. 16:168-175 |
| ISSN: | 1860-4749 1000-9000 |
| DOI: | 10.1007/bf02950421 |
| Popis: | Information filtering (IF) systems are important for personalized information service. However, most current IF systems suffer from low quality and long training time. In this paper, a refined evolving information filtering method is presented. This method describes user’s information need from multi-aspects and improves filtering quality through a process like natural selection. Experimental result shows this method can shorten training time, improve filtering quality, and reduce the relevance between filtering results and training sequence. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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