{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:42:27Z","timestamp":1784828547303,"version":"3.55.0"},"reference-count":43,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2017,2,1]],"date-time":"2017-02-01T00:00:00Z","timestamp":1485907200000},"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":[[2018,6]]},"abstract":"<jats:p>The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users\u2019 actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: \u2018Why is collaborative filtering algorithm not effective?\u2019 and \u2018Do quantitative data such as product rating or purchase history reflect users\u2019 actual tastes?\u2019<\/jats:p>","DOI":"10.1177\/0165551517692955","type":"journal-article","created":{"date-parts":[[2017,2,14]],"date-time":"2017-02-14T10:15:46Z","timestamp":1487067346000},"page":"331-344","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":49,"title":["Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review"],"prefix":"10.1177","volume":"44","author":[{"given":"Youdong","family":"Yun","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danial","family":"Hooshyar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaechoon","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heuiseok","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2017,2,1]]},"reference":[{"key":"bibr1-0165551517692955","doi-asserted-by":"publisher","DOI":"10.3745\/KTCCS.2015.4.9.289"},{"key":"bibr2-0165551517692955","doi-asserted-by":"publisher","DOI":"10.21184\/jkeia.2016.04.10.2.27"},{"issue":"2","key":"bibr3-0165551517692955","first-page":"27","volume":"18","author":"Jeong SM","year":"2016","journal-title":"Int Commer Inf Rev"},{"issue":"3","key":"bibr4-0165551517692955","first-page":"49","volume":"16","author":"Kim HM","year":"2014","journal-title":"Inf Syst Rev"},{"key":"bibr5-0165551517692955","doi-asserted-by":"publisher","DOI":"10.1561\/1100000009"},{"issue":"7","key":"bibr6-0165551517692955","first-page":"768","volume":"41","author":"Kim JW","year":"2016","journal-title":"J Korean Inst Commun Inf Sci"},{"key":"bibr7-0165551517692955","doi-asserted-by":"publisher","DOI":"10.14801\/jkiit.2016.14.8.89"},{"key":"bibr8-0165551517692955","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963772"},{"issue":"4","key":"bibr9-0165551517692955","first-page":"47","volume":"25","author":"Son CH","year":"2010","journal-title":"J Bus Res"},{"key":"bibr10-0165551517692955","unstructured":"Zhang Z, Zhang D, Lai J. urCF: user review enhanced collaborative filtering, 2014, http:\/\/userpages.umbc.edu\/~zzhang3\/papers\/urCF_TonyZhang.pdf"},{"key":"bibr11-0165551517692955","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516667651"},{"key":"bibr12-0165551517692955","doi-asserted-by":"publisher","DOI":"10.1177\/0165551514524675"},{"key":"bibr13-0165551517692955","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2013.09.003"},{"key":"bibr14-0165551517692955","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324999002181"},{"key":"bibr15-0165551517692955","doi-asserted-by":"crossref","unstructured":"Turney PD. 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