| dc.contributor.author | Dhillon, Paramveer S | |
| dc.contributor.author | Aral, Sinan | |
| dc.date.accessioned | 2022-07-27T14:57:51Z | |
| dc.date.available | 2022-07-27T14:57:51Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/144068 | |
| dc.description.abstract | <jats:p> We propose an interpretable model that combines the simplicity of matrix factorization with the flexibility of neural networks to model evolving user interests by efficiently extracting nonlinear patterns from massive text data collections. </jats:p> | en_US |
| dc.language.iso | en | |
| dc.publisher | Institute for Operations Research and the Management Sciences (INFORMS) | en_US |
| dc.relation.isversionof | 10.1287/MKSC.2021.1293 | en_US |
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
| dc.source | arXiv | en_US |
| dc.title | Modeling Dynamic User Interests: A Neural Matrix Factorization Approach | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Dhillon, Paramveer S and Aral, Sinan. 2021. "Modeling Dynamic User Interests: A Neural Matrix Factorization Approach." Marketing Science, 40 (6). | |
| dc.contributor.department | Sloan School of Management | |
| dc.relation.journal | Marketing Science | en_US |
| dc.eprint.version | Author's final manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2022-07-27T12:51:23Z | |
| dspace.orderedauthors | Dhillon, PS; Aral, S | en_US |
| dspace.date.submission | 2022-07-27T12:51:25Z | |
| mit.journal.volume | 40 | en_US |
| mit.journal.issue | 6 | en_US |
| mit.license | OPEN_ACCESS_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
