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 |
|