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.
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2017-ws34dlhd-reranking-based-recommender-system-with-deep-learning.pdf
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- Publication date:
- January 4, 2018
- DOI:
-
- Keyword(s):
- recommender systems deep learning semantic profiling
- Grants:
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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