{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T07:01:00Z","timestamp":1776582060541,"version":"3.51.2"},"reference-count":72,"publisher":"Emerald","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,1,18]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>\u2013 The purpose of this paper is to improve users\u2019 search results relevancy by manipulating their explicit feedback.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>\u2013 CoRRe \u2013 an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, focussing on 20 queries.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>\u2013 Both Mean Average Precision and Normalized Discounted Cumulative Gain results indicate CoRRe to have the highest retrieval precisions at all the three levels compared to the other feedback models. Furthermore, independent t-tests showed the precision differences to be significant. Rating was found to be the most popular technique among the participants, producing the best precision compared to referral and comments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Research limitations\/implications<\/jats:title>\n                  <jats:p>\u2013 The findings suggest that search retrieval relevance can be significantly improved when users\u2019 explicit feedback are integrated, therefore web-based systems should find ways to manipulate users\u2019 feedback to provide better recommendations or search results to the users.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>\u2013 The study is novel in the sense that users\u2019 comment, rating and referral were taken into consideration to improve their overall search experience.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/ajim-07-2015-0106","type":"journal-article","created":{"date-parts":[[2015,12,14]],"date-time":"2015-12-14T05:21:35Z","timestamp":1450070495000},"page":"76-98","source":"Crossref","is-referenced-by-count":9,"title":["Improving retrieval relevance using users\u2019 explicit feedback"],"prefix":"10.1108","volume":"68","author":[{"given":"Vimala","family":"Balakrishnan","sequence":"first","affiliation":[{"name":"University of Malaya, Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kian","family":"Ahmadi","sequence":"additional","affiliation":[{"name":"University of Malaya, Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sri Devi","family":"Ravana","sequence":"additional","affiliation":[{"name":"University of Malaya, Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"1","key":"2025072816220925200_b1","first-page":"39","article-title":"Case-based reasoning: foundational issues, methodological variations, and systems approaches","volume":"7","author":"","year":"1996","journal-title":"Artificial Intelligence Communications"},{"key":"2025072816220925200_b3","doi-asserted-by":"crossref","unstructured":"Agichtein, E.\n          , Brill, E. and Dumais, S. 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