{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T08:01:39Z","timestamp":1771920099736,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T00:00:00Z","timestamp":1555545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2015R1D1A1A01060950"],"award-info":[{"award-number":["NRF-2015R1D1A1A01060950"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Trade, industry &amp; Energy (MI, Korea)","award":["the Technology Innovation Program (or Industrial Strategic technology development program, 10060086, Technology Innovation Program"],"award-info":[{"award-number":["the Technology Innovation Program (or Industrial Strategic technology development program, 10060086, Technology Innovation Program"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/sym11040561","type":"journal-article","created":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T11:58:21Z","timestamp":1555588701000},"page":"561","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Boosting Memory-Based Collaborative Filtering Using Content-Metadata"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1044-3089","authenticated-orcid":false,"given":"Kyung Soo","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doo Soo","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9042-0599","authenticated-orcid":false,"given":"Yong Suk","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schafer, J.B., Frankowski, D., Herlocker, J., and Sen, S. 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