Conference paper Open Access

Content Recommendation through Semantic Annotation of User Reviews and Linked Data

Vagliano, Iacopo; Monti, Diego; Scherp, Ansgar; Morisio, Maurizio


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    "description": "<p>Nowadays, most recommender systems exploit user-provided ratings to infer their preferences. However, the growing popularity of social and e-commerce websites has encouraged users to also share comments and opinions through textual reviews. In this paper, we introduce a new recommendation approach which exploits the semantic annotation of user reviews to extract useful and non-trivial information about the items to recommend. It also relies on the knowledge freely available in the Web of Data, notably in DBpedia and Wikidata, to discover other resources connected with the annotated entities. We evaluated our approach in three domains, using both DBpedia and Wikidata. The results showed that our solution provides a better ranking than another recommendation method based on the Web of Data, while it improves in novelty with respect to traditional techniques based on ratings.</p>", 
    "license": {
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    "title": "Content Recommendation through Semantic Annotation of User Reviews and Linked Data", 
    "notes": "Extended technical report available at https://arxiv.org/abs/1709.09973", 
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    "keywords": [
      "DBpedia, Linked Data, Recommender Systems, Semantic Annotation, Semantic Web, User Reviews, Web of Data, Wikidata"
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    "publication_date": "2018-01-23", 
    "creators": [
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        "affiliation": "ZBW - Leibniz Information Centre for Economics", 
        "name": "Vagliano, Iacopo"
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      {
        "affiliation": "Politecnico di Torino", 
        "name": "Monti, Diego"
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        "affiliation": "ZBW - Leibniz Information Centre for Economics", 
        "name": "Scherp, Ansgar"
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      {
        "affiliation": "Politecnico di Torino", 
        "name": "Morisio, Maurizio"
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      "acronym": "K-CAP 2017", 
      "dates": "4-6 December 2017", 
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