Показать сокращенную информацию
dc.contributor.author | Nikolenko S. | |
dc.date.accessioned | 2018-09-19T21:31:03Z | |
dc.date.available | 2018-09-19T21:31:03Z | |
dc.date.issued | 2016 | |
dc.identifier.issn | 1613-0073 | |
dc.identifier.uri | https://dspace.kpfu.ru/xmlui/handle/net/144087 | |
dc.description.abstract | In this work, we compare two extensions of two different topic models for the same problem of recommending full-Text items: previously developed SVD-LDA and its counterpart SVD-ARTM based on additive regularization. We show that ARTM naturally leads to the inference algorithm that has to be painstakingly developed for LDA. | |
dc.relation.ispartofseries | CEUR Workshop Proceedings | |
dc.title | ARTM vs. LDA: An SVD extension case study | |
dc.type | Conference Paper | |
dc.relation.ispartofseries-volume | 1710 | |
dc.collection | Публикации сотрудников КФУ | |
dc.relation.startpage | 276 | |
dc.source.id | SCOPUS16130073-2016-1710-SID85017192438 |