{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T11:11:46Z","timestamp":1776251506865,"version":"3.50.1"},"reference-count":75,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2023,4,25]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Some studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>This study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-04-2022-0172","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T12:26:26Z","timestamp":1662467186000},"page":"199-221","source":"Crossref","is-referenced-by-count":14,"title":["Impact on recommendation performance of online review helpfulness and consistency"],"prefix":"10.1108","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4819-0010","authenticated-orcid":false,"given":"Jaeseung","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8709-6751","authenticated-orcid":false,"given":"Xinzhe","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6720-7765","authenticated-orcid":false,"given":"Qinglong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2833-5673","authenticated-orcid":false,"given":"Jaekyeong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"issue":"4","key":"key2023062207404500400_ref001","doi-asserted-by":"crossref","first-page":"8324","DOI":"10.1016\/j.eswa.2008.10.029","article-title":"A collaborative filtering method based on artificial immune network","volume":"36","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"5","key":"key2023062207404500400_ref002","first-page":"896","article-title":"Improving aggregate recommendation diversity using ranking-based techniques","volume":"24","year":"2011","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"5","key":"key2023062207404500400_ref003","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1007\/s10796-020-10030-7","article-title":"Online review consistency matters: an elaboration likelihood model perspective","volume":"23","year":"2021","journal-title":"Information Systems Frontiers"},{"key":"key2023062207404500400_ref004","article-title":"The Netflix Prize","year":"2007"},{"key":"key2023062207404500400_ref005","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","year":"2013","journal-title":"Knowledge-Based Systems"},{"key":"key2023062207404500400_ref006","doi-asserted-by":"crossref","unstructured":"Castells, P., Hurley, N.J. and Vargas, S. 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