{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:27:52Z","timestamp":1777854472890,"version":"3.51.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2013,5,23]],"date-time":"2013-05-23T00:00:00Z","timestamp":1369267200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2013,12]]},"abstract":"<jats:p>Relevance feedback (RF) has been studied under laboratory conditions using test collections and either test persons or simple simulation. These studies have given mixed results. Automatic (or pseudo) RF and intellectual RF, both leading to query reformulation, are the main approaches to explicit RF. In the present study we perform RF with the help of classification of search results. We conduct our experiments in a comprehensive collection, namely various TREC ad-hoc collections with 250 topics. We also studied various term space reduction techniques for the classification process. The research questions are: given RF on top results of pseudo RF (PRF) query results, is it possible to learn effective classifiers for the following results? What is the effectiveness of various classification methods? Our findings indicate that this approach of applying RF is significantly more effective than PRF with short (title) queries and long (title and description) queries.<\/jats:p>","DOI":"10.1177\/0165551513488317","type":"journal-article","created":{"date-parts":[[2013,5,23]],"date-time":"2013-05-23T21:17:27Z","timestamp":1369343847000},"page":"764-772","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Effectiveness of search result classification based on relevance feedback"],"prefix":"10.1177","volume":"39","author":[{"given":"Feza","family":"Baskaya","sequence":"first","affiliation":[{"name":"School of Information Sciences, University of Tampere, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heikki","family":"Keskustalo","sequence":"additional","affiliation":[{"name":"School of Information Sciences, University of Tampere, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kalervo","family":"J\u00e4rvelin","sequence":"additional","affiliation":[{"name":"School of Information Sciences, University of Tampere, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2013,5,23]]},"reference":[{"issue":"1","key":"bibr1-0165551513488317","first-page":"35","volume":"15","author":"Marchionini G","year":"1993","journal-title":"Library and Information Science Research"},{"key":"bibr2-0165551513488317","first-page":"121","volume":"31","author":"Efthimiadis EN","year":"1996","journal-title":"Annual Review of Information Science and Technology"},{"key":"bibr3-0165551513488317","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888903000638"},{"key":"bibr4-0165551513488317","doi-asserted-by":"publisher","DOI":"10.1002\/asi.10240"},{"key":"bibr5-0165551513488317","doi-asserted-by":"publisher","DOI":"10.1007\/11423270_4"},{"key":"bibr6-0165551513488317","volume-title":"Using text classification method in relevance feedback. 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