Improving retrieval relevance using users’ explicit feedback
Purpose – The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback. Design/methodology/approach – CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is furt...
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| Format: | Article |
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Emerald
2016
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| _version_ | 1825721116315877376 |
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| author | Balakrishnan, Vimala Ahmadi, K. Ravana, S.D. |
| author_facet | Balakrishnan, Vimala Ahmadi, K. Ravana, S.D. |
| author_sort | Balakrishnan, Vimala |
| collection | UM |
| description | Purpose – The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback. Design/methodology/approach – CoRRe – 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. Findings – 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. Research limitations/implications – The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users. Originality/value – The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience. |
| first_indexed | 2024-03-06T05:45:28Z |
| format | Article |
| id | um.eprints-18451 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:45:28Z |
| publishDate | 2016 |
| publisher | Emerald |
| record_format | dspace |
| spelling | um.eprints-184512020-01-08T07:41:54Z http://eprints.um.edu.my/18451/ Improving retrieval relevance using users’ explicit feedback Balakrishnan, Vimala Ahmadi, K. Ravana, S.D. QA75 Electronic computers. Computer science Purpose – The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback. Design/methodology/approach – CoRRe – 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. Findings – 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. Research limitations/implications – The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users. Originality/value – The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience. Emerald 2016 Article PeerReviewed Balakrishnan, Vimala and Ahmadi, K. and Ravana, S.D. (2016) Improving retrieval relevance using users’ explicit feedback. Aslib Journal of Information Management, 68 (1). pp. 76-98. ISSN 2050-3806, DOI https://doi.org/10.1108/AJIM-07-2015-0106 <https://doi.org/10.1108/AJIM-07-2015-0106>. https://doi.org/10.1108/AJIM-07-2015-0106 doi:10.1108/AJIM-07-2015-0106 |
| spellingShingle | QA75 Electronic computers. Computer science Balakrishnan, Vimala Ahmadi, K. Ravana, S.D. Improving retrieval relevance using users’ explicit feedback |
| title | Improving retrieval relevance using users’ explicit feedback |
| title_full | Improving retrieval relevance using users’ explicit feedback |
| title_fullStr | Improving retrieval relevance using users’ explicit feedback |
| title_full_unstemmed | Improving retrieval relevance using users’ explicit feedback |
| title_short | Improving retrieval relevance using users’ explicit feedback |
| title_sort | improving retrieval relevance using users explicit feedback |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT balakrishnanvimala improvingretrievalrelevanceusingusersexplicitfeedback AT ahmadik improvingretrievalrelevanceusingusersexplicitfeedback AT ravanasd improvingretrievalrelevanceusingusersexplicitfeedback |