Conference paper Open Access

Reranking-based Recommender System with Deep Learning

Saleh, Ahmed; Mai, Florian; Nishioka, Chifumi; Scherp, Ansgar


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.1135136", 
  "language": "eng", 
  "title": "Reranking-based Recommender System with Deep Learning", 
  "issued": {
    "date-parts": [
      [
        2018, 
        1, 
        4
      ]
    ]
  }, 
  "abstract": "<p>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.</p>", 
  "author": [
    {
      "family": "Saleh, Ahmed"
    }, 
    {
      "family": "Mai, Florian"
    }, 
    {
      "family": "Nishioka, Chifumi"
    }, 
    {
      "family": "Scherp, Ansgar"
    }
  ], 
  "type": "paper-conference", 
  "id": "1135136"
}
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