Understanding collaborative filtering parameters for personalized recommendations in e-commerce
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hong Joo | - |
dc.contributor.author | Kim, Jong Woo | - |
dc.contributor.author | Park, Sung Joo | - |
dc.date.accessioned | 2022-12-21T05:09:10Z | - |
dc.date.available | 2022-12-21T05:09:10Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2007-12 | - |
dc.identifier.issn | 1389-5753 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179217 | - |
dc.description.abstract | Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized recommendations. However, there has been no in-depth investigation of the parameters of CF in relation to the number of ratings on the part of an individual customer and the total number of ratings for an item. We empirically investigated the relationships between these two parameters and CF performance, using two publicly available data sets, EachMovie and MovieLens. We conducted three experiments. The first two investigated the relationship between a particular customer's number of ratings and CF recommendation performance. The third experiment evaluated the relationship between the total number of ratings for a particular item and CF recommendation performance. We found that there are ratings thresholds below which recommendation performance increases monotonically, i.e., when the numbers of customer and item ratings are below threshold levels, CF recommendation performance is affected. In addition, once rating numbers surpass threshold levels, the value of each rating decreases. These results may facilitate operational decisions when applying CF in practice. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Kluwer Academic Publishers | - |
dc.title | Understanding collaborative filtering parameters for personalized recommendations in e-commerce | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Jong Woo | - |
dc.identifier.doi | 10.1007/s10660-007-9004-7 | - |
dc.identifier.scopusid | 2-s2.0-36248967124 | - |
dc.identifier.bibliographicCitation | Electronic Commerce Research, v.7, no.3-4, pp.293 - 314 | - |
dc.relation.isPartOf | Electronic Commerce Research | - |
dc.citation.title | Electronic Commerce Research | - |
dc.citation.volume | 7 | - |
dc.citation.number | 3-4 | - |
dc.citation.startPage | 293 | - |
dc.citation.endPage | 314 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Collaborative filtering | - |
dc.subject.keywordAuthor | E-Commerce | - |
dc.subject.keywordAuthor | Parameter selection | - |
dc.subject.keywordAuthor | Personalization | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s10660-007-9004-7 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.