Conference paper Open Access
Reranking-based Recommender System with Deep Learning
Saleh, Ahmed; Mai, Florian; Nishioka, Chifumi; Scherp, Ansgar
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.
| Name | Size | |
|---|---|---|
|
2017-ws34dlhd-reranking-based-recommender-system-with-deep-learning.pdf
md5:6498d41cfe3a4d809f5ac788dbedfcbe |
166.1 kB | Download |
| All versions | This version | |
|---|---|---|
| Views | 16 | 16 |
| Downloads | 9 | 9 |
| Data volume | 1.5 MB | 1.5 MB |
| Unique views | 16 | 16 |
| Unique downloads | 9 | 9 |
- Publication date:
- January 4, 2018
- DOI:
-
- Keyword(s):
- recommender systems deep learning semantic profiling
- Grants:
-
European Commission:
- MOVING - Training towards a society of data-savvy information professionals to enable open leadership innovation (693092)
- Meeting:
- 47. Jahrestagung der Gesellschaft für Informatik (INFORMATIK 2017), Chemnitz, Germany, 25-29 September 2017
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International