{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:27:47Z","timestamp":1754155667063,"version":"3.41.2"},"reference-count":103,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2018,5,14]],"date-time":"2018-05-14T00:00:00Z","timestamp":1526256000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2018,5,14]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>To retain consumer attention and increase purchasing rates, many e-commerce vendors have adopted content-based recommender systems. However, apart from text-based documents, there is little theoretical background guiding element selection, resulting in a limited content analysis problem. Another inherent problem is overspecialization. The purpose of this paper is to establish a value-based recommendation methodology for identifying favorable attributes, benefits, and values on the basis of means-end chain theory. The identified elements and the relationships between them were utilized to construct a recommender system without incurring either problem.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This study adopted soft laddering and content analysis to collect popular elements. The relationships between the elements were established by using a hard laddering online questionnaire. The elements and the relationships were utilized to build a hierarchical value map (HVM). A mathematical model was then devised on the basis of the HVM to predict user preferences of attributes.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results of a performance comparison showed that the proposed method outperformed the content-based attribute recommendation method and a hybrid method by 39 and 68 percent, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Although hybrid methods have been proposed to resolve the problem of overspecialization in content-based recommender systems, such methods have incurred \u201ccold start\u201d and \u201csparsity\u201d problems. The proposed method can provide recommendations without causing these problems while outperforming the content-based and hybrid approaches.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-04-2017-0164","type":"journal-article","created":{"date-parts":[[2018,4,13]],"date-time":"2018-04-13T07:38:05Z","timestamp":1523605085000},"page":"765-781","source":"Crossref","is-referenced-by-count":7,"title":["Identifying and recommending user-interested attributes with values"],"prefix":"10.1108","volume":"118","author":[{"given":"Yun-Shan","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Ping-Yu","family":"Hsu","sequence":"additional","affiliation":[]},{"given":"Yu-Chin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"6","key":"key2020092903084752000_ref001","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: a survey of the state-of-the-art and 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