Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최용석 | - |
dc.date.accessioned | 2019-09-30T01:53:56Z | - |
dc.date.available | 2019-09-30T01:53:56Z | - |
dc.date.issued | 2019-04 | - |
dc.identifier.citation | SYMMETRY-BASEL, v. 11, NO 4, no. 561 | en_US |
dc.identifier.issn | 2073-8994 | - |
dc.identifier.uri | https://www.mdpi.com/2073-8994/11/4/561 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/110748 | - |
dc.description.abstract | Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment. | en_US |
dc.description.sponsorship | This work was supported by the Technology Innovation Program(No. 10060086,10077553) funded by the MOTIE(Ministry of Trade, industry & Energy, Korea), by the MSIT(Ministry of Science and ICT, Korea) under the ITRC(Information Technology Research Center) support program(IITP-2017-0-01642) supervised by the IITP(Institute for Information & communications Technology Promotion), and by the NRF(National Research Foundation) grant funded by the Korea government(MSIT) (No. 2018R1A5A7059549). | en_US |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.subject | collaborative filtering | en_US |
dc.subject | content-metadata | en_US |
dc.subject | user-content rating | en_US |
dc.title | Boosting Memory-Based Collaborative Filtering Using Content-Metadata | en_US |
dc.type | Article | en_US |
dc.relation.no | 4 | - |
dc.relation.volume | 11 | - |
dc.identifier.doi | 10.3390/sym11040561 | - |
dc.relation.page | 1-18 | - |
dc.relation.journal | SYMMETRY-BASEL | - |
dc.contributor.googleauthor | Kim, Kyung Soo | - |
dc.contributor.googleauthor | Chang, Doo Soo | - |
dc.contributor.googleauthor | Choi, Yong Suk | - |
dc.relation.code | 2019043270 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF COMPUTER SCIENCE | - |
dc.identifier.pid | cys | - |
dc.identifier.orcid | https://orcid.org/0000-0002-9042-0599 | - |
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