Reranking-based Recommender System with Deep Learning
| dc.contributor.author | Saleh, Ahmed | |
| dc.contributor.author | Mai, Florian | |
| dc.contributor.author | Nishioka, Chifumi | |
| dc.contributor.author | Scherp, Ansgar | |
| dc.contributor.editor | Eibl, Maximilian | |
| dc.contributor.editor | Gaedke, Martin | |
| dc.date.accessioned | 2017-08-28T23:47:40Z | |
| dc.date.available | 2017-08-28T23:47:40Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | An enormous volume of scientific content is published every year. The amount exceeds by far what a scientist can read in her entire life. In order to address this problem, we have developed and empirically evaluated a recommender system for scientific papers based on Twitter postings. In this paper, we improve on the previous work by a reranking approach using Deep Learning. Thus, after a list of top-k recommendations is computed, we rerank the results by employing a neural network to improve the results of the existing recommender system. We present the design of the deep reranking approach and a preliminary evaluation. Our results show that in most cases, the recommendations can be improved using our Deep Learning reranking approach. | en |
| dc.identifier.doi | 10.18420/in2017_216 | |
| dc.identifier.isbn | 978-3-88579-669-5 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2017 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-275 | |
| dc.subject | recommender systems | |
| dc.subject | deep learning | |
| dc.subject | semantic profiling | |
| dc.title | Reranking-based Recommender System with Deep Learning | en |
| gi.citation.endPage | 2175 | |
| gi.citation.startPage | 2169 | |
| gi.conference.date | 25.-29. September 2017 | |
| gi.conference.location | Chemnitz | |
| gi.conference.sessiontitle | Deep Learning in heterogenen Datenbeständen |
Dateien
Originalbündel
1 - 1 von 1