{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T09:00:30Z","timestamp":1772182830615,"version":"3.50.1"},"reference-count":45,"publisher":"Emerald","issue":"10","license":[{"start":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T00:00:00Z","timestamp":1509926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2017,11,6]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to present a novel framework for recommending desirable products to active customers with the consideration of not only their preferences but also the products\u2019 quality performances and their e-retailers\u2019 service performances under e-commerce.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A framework in support of the product recommendation is presented. Three modules are involved in the framework, i.e. data collection and preference analysis module, hybrid recommendation module and recommendation generation module. First, preferences of different types of customers are inferred through analysis of their behavioral data and then a paradigm is adopted based on cased-based reasoning to generate candidate recommendation products. Further, recommendation lists for different types of customers are obtained through measurement of the quality and service performances concerning each candidate recommendation product.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>To illustrate the performance of the presented framework, a simulation study comparing the approach developed based on the framework and the traditional approach is conducted. The experiment results show that the developed approach outperformed the traditional approach in term of average rank score. This means that incorporating the consideration of product performance and customer service factors can play an important role in product recommendations.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The presented framework can overcome the defect that low conversion rate of recommended products to actually purchased ones suffered by the traditional approach. In addition, the use of the presented framework can not only help customers to obtain desirable products and save searching time but also supervise and urge e-retailers to pay more attention to the quality and service performances.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-03-2017-0096","type":"journal-article","created":{"date-parts":[[2017,11,22]],"date-time":"2017-11-22T06:32:07Z","timestamp":1511332327000},"page":"1753-1776","source":"Crossref","is-referenced-by-count":7,"title":["Product recommendation incorporating the consideration of product performance and customer service factors"],"prefix":"10.1108","volume":"46","author":[{"given":"Yong-Hai","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi-Ping","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang-Hui","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020120500130026900_ref001","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.knosys.2016.03.006","article-title":"User profiling approaches for demographic recommender systems","volume":"100","year":"2016","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"key2020120500130026900_ref002","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.ipm.2012.07.008","article-title":"Cluster searching strategies for collaborative recommendation systems","volume":"49","year":"2013","journal-title":"Information Processing & Management"},{"issue":"22","key":"key2020120500130026900_ref003","doi-asserted-by":"crossref","first-page":"4290","DOI":"10.1016\/j.ins.2010.07.024","article-title":"A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition","volume":"180","year":"2010","journal-title":"Information Sciences"},{"issue":"12","key":"key2020120500130026900_ref004","doi-asserted-by":"crossref","first-page":"14609","DOI":"10.1016\/j.eswa.2011.05.021","article-title":"A framework for collaborative filtering recommender systems","volume":"38","year":"2011","journal-title":"Expert Systems with Applications"},{"key":"key2020120500130026900_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"},{"issue":"4","key":"key2020120500130026900_ref006","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1023\/a:1021240730564","article-title":"Hybrid recommender systems: survey and experiments","volume":"12","year":"2002","journal-title":"User Modeling and User-Adapted Interaction"},{"issue":"12","key":"key2020120500130026900_ref007","doi-asserted-by":"crossref","first-page":"10990","DOI":"10.1016\/j.eswa.2012.03.025","article-title":"Social knowledge-based recommender system. 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