CrowdStart: Warming up cold-start items using crowdsourcing
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Dong-Gyun | - |
dc.contributor.author | Lee, Yeon-Chang | - |
dc.contributor.author | Lee, Jongwuk | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2022-07-08T23:43:01Z | - |
dc.date.available | 2022-07-08T23:43:01Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146713 | - |
dc.description.abstract | The cold-start problem is one of the critical challenges in personalized recommender systems. A lot of existing work has been studied to exploit a user-item rating matrix as well as additional information for users/items, e.g., user profiles, item contents, and social relationships among users. However, because existing work is primarily biased to the auxiliary information for users/items, it is difficult to identify various and reliable item neighbors that are relevant to cold-start items. To alleviate this limitation, we propose a new crowd-enabled framework, called CrowdStart, which is an integrated human-machine approach for new item recommendation. The main contributions of the CrowdStart framework are twofold: (1) To find various and reliable item neighbors for new items, we design two-step crowdsourcing tasks that harness not only machine-only algorithms but also the knowledge of crowd workers (including a few experts and a large number of non-expert workers in a crowdsourcing platform). (2) We develop a novel hybrid model to exploit the user-item rating matrix, the content information about items, and the crowd-based item neighbors from human knowledge into new item recommendation. To evaluate the effectiveness of the CrowdStart framework, we conduct extensive experiments including both a user study and simulation tests. Through the empirical study, we found that the CrowdStart framework provides relevant, diverse, reliable, and explainable crowd-based neighbors for new items and the crowd-based neighbors are meaningful for improving the accuracy of new item recommendation. The datasets and detailed experimental results are available at https://goo.gl/1iXTUE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | CrowdStart: Warming up cold-start items using crowdsourcing | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1016/j.eswa.2019.07.030 | - |
dc.identifier.scopusid | 2-s2.0-85069592969 | - |
dc.identifier.wosid | 000489189900016 | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.138, pp.1 - 15 | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 138 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 15 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | RECOMMENDER | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordAuthor | Collaborative filtering | - |
dc.subject.keywordAuthor | New item recommendation | - |
dc.subject.keywordAuthor | Crowdsourcing | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0957417419305093?via%3Dihub | - |
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