Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김상욱 | - |
dc.date.accessioned | 2021-02-17T01:55:46Z | - |
dc.date.available | 2021-02-17T01:55:46Z | - |
dc.date.issued | 2019-12 | - |
dc.identifier.citation | EXPERT SYSTEMS WITH APPLICATIONS, v. 138, article no. 112813 | en_US |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.issn | 1873-6793 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0957417419305093?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/158527 | - |
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. (C) 2019 Elsevier Ltd. All rights reserved. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT; Ministry of Science and ICT) (NRF-2017R1A2B3004581, 2018R1A2B6009135, and 2018R1A5A7059549) and Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017M3C4A7083678). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (no. 2019-0-00421, AI Graduate School Support Program). | en_US |
dc.language.iso | en | en_US |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
dc.subject | Collaborative filtering | en_US |
dc.subject | New item recommendation | en_US |
dc.subject | Crowdsourcing | en_US |
dc.title | CrowdStart: Warming up cold-start items using crowdsourcing | en_US |
dc.type | Article | en_US |
dc.relation.volume | 138 | - |
dc.identifier.doi | 10.1016/j.eswa.2019.07.030 | - |
dc.relation.page | 1-15 | - |
dc.relation.journal | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.contributor.googleauthor | Hong, Dong-Gyun | - |
dc.contributor.googleauthor | Lee, Yeon-Chang | - |
dc.contributor.googleauthor | Lee, Jongwuk | - |
dc.contributor.googleauthor | Kim, Sang-Wook | - |
dc.relation.code | 2019037741 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF COMPUTER SCIENCE | - |
dc.identifier.pid | wook | - |
dc.identifier.orcid | https://orcid.org/0000-0002-6345-9084 | - |
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