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Reranking-based Recommender System with Deep Learning

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2017

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Gesellschaft für Informatik, Bonn

Zusammenfassung

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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Saleh, Ahmed; Mai, Florian; Nishioka, Chifumi; Scherp, Ansgar (2017): Reranking-based Recommender System with Deep Learning. INFORMATIK 2017. DOI: 10.18420/in2017_216. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-669-5. pp. 2169-2175. Deep Learning in heterogenen Datenbeständen. Chemnitz. 25.-29. September 2017

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