DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Kiwon | ko |
dc.contributor.author | Jo, Hyeonsoo | ko |
dc.contributor.author | Kim, Hyoji | ko |
dc.contributor.author | Lee, Yong Hoon | ko |
dc.date.accessioned | 2023-08-17T02:00:53Z | - |
dc.date.available | 2023-08-17T02:00:53Z | - |
dc.date.created | 2023-07-07 | - |
dc.date.issued | 2019-10 | - |
dc.identifier.citation | 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 | - |
dc.identifier.issn | 2161-0363 | - |
dc.identifier.uri | http://hdl.handle.net/10203/311602 | - |
dc.description.abstract | In recent years, most recommender systems rely on collaborative filtering (CF) based on matrix factorization (MF) that can predict unknown ratings by completing a rating matrix. However, this approach cannot be used for the cold start where no rating information is available for a given user or item. To address this problem, we develop a new hybrid CF (HCF) technique incorporating CF with content information. The proposed HCF is based on an auto-encoder (AE) consisting of a nonlinear encoder and a linear decoder. This type of AE is called the basis learning AE (BAE), because it can learn the basis of the row space of a sparse input matrix by its encoder. In the proposed scheme, the input to the BAE is a content augmented rating matrix; the BAE learns the basis of the row space of a given rating matrix, which is a subset of the basis of the content augmented rating matrix, and recovers each row of the rating matrix by a linear combination of the learned basis. Unlike most existing HCF schemes, our model does not incorporate additional content-based objective terms; yet extensive experiments on real-world datasets show that the proposed HCF can significantly advance the state-of-the-art. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Basis Learning Autoencoders for Hybrid Collaborative Filtering in Cold Start Setting | - |
dc.type | Conference | - |
dc.identifier.wosid | 000534480500062 | - |
dc.identifier.scopusid | 2-s2.0-85077707410 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Pittsburgh, PA | - |
dc.identifier.doi | 10.1109/MLSP.2019.8918843 | - |
dc.contributor.localauthor | Lee, Yong Hoon | - |
dc.contributor.nonIdAuthor | Kim, Hyoji | - |
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