DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Seunghyeon | ko |
dc.contributor.author | Lee, Jongwuk | ko |
dc.contributor.author | Shim, Hyunjung | ko |
dc.date.accessioned | 2022-11-09T03:00:20Z | - |
dc.date.available | 2022-11-09T03:00:20Z | - |
dc.date.created | 2022-07-07 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.citation | 2019 World Wide Web Conference, WWW 2019, pp.863 - 873 | - |
dc.identifier.uri | http://hdl.handle.net/10203/299404 | - |
dc.description.abstract | Implicit user feedback is a fundamental dataset for personalized recommendation models. Because of its inherent characteristics of sparse one-class values, it is challenging to uncover meaningful user/item representations. In this paper, we propose dual neural personalized ranking (DualNPR), which fully exploits both user- and item-side pairwise rankings in a unified manner. The key novelties of the proposed model are three-fold: (1) DualNPR discovers mutual correlation among users and items by utilizing both user- and item-side pairwise rankings, alleviating the data sparsity problem. We stress that, unlike existing models that require extra information, DualNPR naturally augments both user- and item-side pairwise rankings from a user-item interaction matrix. (2) DualNPR is built upon deep matrix factorization to capture the variability of user/item representations. In particular, it chooses raw user/item vectors as an input and learns latent user/item representations effectively. (3) DualNPR employs a dynamic negative sampling method using an exponential function, further improving the accuracy of top-N recommendation. In experimental results over three benchmark datasets, DualNPR outperforms baseline models by 21.9-86.7% in hit rate, 14.5-105.8% in normalized discounted cumulative gain, and 5.1-23.3% in the area under the ROC curve. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Dual neural personalized ranking | - |
dc.type | Conference | - |
dc.identifier.wosid | 000483508400081 | - |
dc.identifier.scopusid | 2-s2.0-85066911950 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 863 | - |
dc.citation.endingpage | 873 | - |
dc.citation.publicationname | 2019 World Wide Web Conference, WWW 2019 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | San Francisco, CA | - |
dc.identifier.doi | 10.1145/3308558.3313585 | - |
dc.contributor.localauthor | Shim, Hyunjung | - |
dc.contributor.nonIdAuthor | Kim, Seunghyeon | - |
dc.contributor.nonIdAuthor | Lee, Jongwuk | - |
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