Search Engines that learn from implicit feedback /
Search-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular u...
| Autor principal: | |
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| Formato: | Otro (Other) |
| Idioma: | Español (Spanish) |
| Materias: |
| _version_ | 1775173581949370368 |
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| author | 104399 Joachims, Thorsten |
| author_facet | 104399 Joachims, Thorsten |
| author_sort | 104399 Joachims, Thorsten |
| collection | Catálogo |
| coutry_str | Colombia |
| description | Search-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular user group or collection. Each time a user formulates a query or clicks on a search result, easily observable feedback is provided to the search engine. Unlike surveys or other types of explicit feedback, this implicit feedback is essentially free, reflects the search engine's natural use, and is specific to a particular user and collection |
| first_indexed | 2023-05-12T18:45:52Z |
| format | Otro (Other) |
| id | KOHA-UCATOLICA:12663 |
| institution | Universidad Católica de Colombia |
| language | Español (Spanish) |
| last_indexed | 2023-08-08T07:52:33Z |
| record_format | koha |
| spelling | KOHA-UCATOLICA:126632023-08-01T22:23:01ZSearch Engines that learn from implicit feedback / 104399 Joachims, Thorsten textospaSearch-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular user group or collection. Each time a user formulates a query or clicks on a search result, easily observable feedback is provided to the search engine. Unlike surveys or other types of explicit feedback, this implicit feedback is essentially free, reflects the search engine's natural use, and is specific to a particular user and collectionSearch-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular user group or collection. Each time a user formulates a query or clicks on a search result, easily observable feedback is provided to the search engine. Unlike surveys or other types of explicit feedback, this implicit feedback is essentially free, reflects the search engine's natural use, and is specific to a particular user and collectionMOTORES DE BUSQUEDASISTEMAS DE ALMACENAMIENTO Y RECUPERACION DE INFORMACIONIEEE ComputerElectrónica y TelecomunicacionesSistemas |
| spellingShingle | MOTORES DE BUSQUEDA SISTEMAS DE ALMACENAMIENTO Y RECUPERACION DE INFORMACION 104399 Joachims, Thorsten Search Engines that learn from implicit feedback / |
| title | Search Engines that learn from implicit feedback / |
| title_full | Search Engines that learn from implicit feedback / |
| title_fullStr | Search Engines that learn from implicit feedback / |
| title_full_unstemmed | Search Engines that learn from implicit feedback / |
| title_short | Search Engines that learn from implicit feedback / |
| title_sort | search engines that learn from implicit feedback / |
| topic | MOTORES DE BUSQUEDA SISTEMAS DE ALMACENAMIENTO Y RECUPERACION DE INFORMACION |
| work_keys_str_mv | AT 104399joachimsthorsten searchenginesthatlearnfromimplicitfeedback |