Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김상욱 | - |
dc.date.accessioned | 2018-04-03T00:24:22Z | - |
dc.date.available | 2018-04-03T00:24:22Z | - |
dc.date.issued | 2014-04 | - |
dc.identifier.citation | WWW '14 Companion Proceedings of the 23rd International Conference on World Wide Web, Pages 299-300 | en_US |
dc.identifier.isbn | 978-1-4503-2745-9 | - |
dc.identifier.uri | https://dl.acm.org/citation.cfm?doid=2567948.2577363 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/55242 | - |
dc.description.abstract | Recommendation methods suffer from the data sparsity and cold-start user problems, often resulting in low accuracy. To address these problems, we propose a novel imputation method, which effectively densifies a rating matrix by filling unevaluated ratings with probable values. In our method, we use a trust network to estimate the unevaluated ratings accurately. We conduct experiments on the Epinions dataset and demonstrate that our method helps provide better recommendation accuracy than previous methods, especially for cold-start users. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ACM New York, NY, USA | en_US |
dc.subject | Data imputation | en_US |
dc.subject | Matrix factorization | en_US |
dc.subject | Recommendation system | en_US |
dc.subject | Trust network | en_US |
dc.title | Data Imputation Using a Trust Network for Recommendation | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1145/2567948.2577363 | - |
dc.relation.page | 299-300 | - |
dc.contributor.googleauthor | Hwang, W.-S. | - |
dc.contributor.googleauthor | Li, S. | - |
dc.contributor.googleauthor | Kim, S.-W. | - |
dc.contributor.googleauthor | Lee, K | - |
dc.relation.code | 20140023 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF COMPUTER SCIENCE | - |
dc.identifier.pid | wook | - |
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