| dc.contributor.author | Capan, G. | |
| dc.contributor.author | Yılmazel, Özgür | |
| dc.date.accessioned | 2019-10-21T20:10:58Z | |
| dc.date.available | 2019-10-21T20:10:58Z | |
| dc.date.issued | 2011 | |
| dc.identifier.issn | 1613-0073 | |
| dc.identifier.uri | https://hdl.handle.net/11421/20011 | |
| dc.description | ECML/PKDD Discovery Challenge Workshop 2011, DCW 2011 -- 5 September 2011 through 5 September 2011 -- Athens -- 101687 | en_US |
| dc.description.abstract | Recommender systems are popular information filtering systems used in various domains. Cold-start problem is a key challenge in a recommender system. In newitem/existing-user case of the cold-start problem, which is recommendation of a recently-arrived item to a user with historical data, finding links between existing items with recently-arrived items is critical. Using VideoLectures.net Cold-Start Recommendation Challenge data, this paper includes a linear regression model to predict future co-viewing count between an existing item and a recently-arrived, not-yet-viewed item. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | CEUR-WS | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.title | Joint features regression for Cold-Start Recommendation on VideoLectures.Net | en_US |
| dc.type | conferenceObject | en_US |
| dc.relation.journal | CEUR Workshop Proceedings | en_US |
| dc.contributor.department | Anadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.identifier.volume | 770 | en_US |
| dc.identifier.startpage | 103 | en_US |
| dc.identifier.endpage | 109 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.contributor.institutionauthor | Yılmazel, Özgür | |
