DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Kiwon | ko |
dc.contributor.author | Lee, Yong Hoon | ko |
dc.contributor.author | Suh, Changho | ko |
dc.date.accessioned | 2018-12-20T02:13:53Z | - |
dc.date.available | 2018-12-20T02:13:53Z | - |
dc.date.created | 2018-12-03 | - |
dc.date.created | 2018-12-03 | - |
dc.date.created | 2018-12-03 | - |
dc.date.issued | 2018-06-05 | - |
dc.identifier.citation | 2018 IEEE Data Science Workshop (DSW), pp.130 - 134 | - |
dc.identifier.uri | http://hdl.handle.net/10203/247476 | - |
dc.description.abstract | We consider autoencoders (AEs) for matrix completion (MC) with application to collaborative filtering (CF) for recommedation systems. It is observed that for a given sparse user-item rating matrix, denoted asM, an AE performs matrix factorization so that the recovered matrix is represented as a product of user and item feature matrices. Such an AE sequentially estimates user and item feature matrices: for the item-based AE (I-AE) that uses columns of M as its input vectors, the AE’s encoder first estimates an item feature matrix and then the decoder estimates a user feature matrix based on the output of the encoder. Similarly, the user-based AE (U-AE) that uses the columns ofMT as its input vectors first estimates a user feature matrix and then an item feature matrix. This sequential estimation can degrade the performance of the MC/CF, because the decoder depends on the output of the encoder. To enhance MC/CF performance, we propose alternating AEs (AAEs), a parallel algorithm employing both I-AE and U-AE and alternatively use them. We apply the AAE to synthetic, MovieLens 100k and 1M data sets. The results demonstrate that AAE can outperform all existing MC/CF methods. | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.title | ALTERNATING AUTOENCODERS FOR MATRIX COMPLETION | - |
dc.type | Conference | - |
dc.identifier.wosid | 000520066100027 | - |
dc.identifier.scopusid | 2-s2.0-85053111477 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 130 | - |
dc.citation.endingpage | 134 | - |
dc.citation.publicationname | 2018 IEEE Data Science Workshop (DSW) | - |
dc.identifier.conferencecountry | SZ | - |
dc.identifier.conferencelocation | EPFL, Lausanne | - |
dc.identifier.doi | 10.1109/DSW.2018.8439121 | - |
dc.contributor.localauthor | Lee, Yong Hoon | - |
dc.contributor.localauthor | Suh, Changho | - |
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