Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김영훈 | - |
dc.date.accessioned | 2023-01-03T05:21:33Z | - |
dc.date.available | 2023-01-03T05:21:33Z | - |
dc.date.issued | 2014-12 | - |
dc.identifier.citation | Proceedings - IEEE International Conference on Data Mining, ICDM 2015, article no. 7023415, Page. 869-874 | - |
dc.identifier.issn | 1550-4786 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/7023415 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/178603 | - |
dc.description.abstract | Ranking objects is an essential problem in recommendation systems. Since comparing two objects is the simplest type of queries in order to measure the relevance of objects, the problem of aggregating pair wise comparisons to obtain a global ranking has been widely studied. In order to learn a ranking model, a training set of queries as well as their correct labels are supplied and a machine learning algorithm is used to find the appropriate parameters of the ranking model with respect to the labels. In this paper, we propose a probabilistic model for learning multiple latent rankings using pair wise comparisons. Our novel model can capture multiple hidden rankings underlying the pair wise comparisons. Based on the model, we develop an efficient inference algorithm to learn multiple latent rankings. The performance study with synthetic and real-life data sets confirms the effectiveness of our model and inference algorithm. © 2014 IEEE. | - |
dc.description.sponsorship | This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2012M3C4A7033342), and also supported by ITRC(Information Technology Research Center) Program under the supervision of NIPA(NIPA-2014-H0301-14-1022). | - |
dc.language | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.subject | Learning to rank | - |
dc.subject | multiple latent rankings | - |
dc.subject | supervised learning | - |
dc.title | Latent Ranking Analysis Using Pairwise Comparisons | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICDM.2014.77 | - |
dc.relation.page | 869-874 | - |
dc.relation.journal | Proceedings - IEEE International Conference on Data Mining, ICDM 2015 | - |
dc.contributor.googleauthor | Kim, Younghoon | - |
dc.contributor.googleauthor | Kim, Wooyeol | - |
dc.contributor.googleauthor | Shim, Kyuseok | - |
dc.sector.campus | E | - |
dc.sector.daehak | 소프트웨어융합대학 | - |
dc.sector.department | 인공지능학과 | - |
dc.identifier.pid | nongaussian | - |
dc.identifier.article | 7023415 | - |
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