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Publication . Conference object . 2017

Content Recommendation through Semantic Annotation of User Reviews and Linked Data

Iacopo Vagliano; Diego Monti; Ansgar Scherp; Maurizio Morisio;
Open Access
Published: 01 Jan 2017
Country: Italy
Abstract
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.
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Popularity Semantic annotation Novelty Semantic Web Ranking (information retrieval) Information retrieval Recommender system Linked data Exploit

Subjects

DBpedia, Linked Data, Recommender Systems, Semantic Annotation, Semantic Web, User Reviews, Web of Data, Wikidata

Funded by
EC| MOVING
Project
MOVING
Training towards a society of data-savvy information professionals to enable open leadership innovation
  • Funder: European Commission (EC)
  • Project Code: 693092
  • Funding stream: H2020 | RIA
Validated by funder
,
EC| MOVING
Project
MOVING
Training towards a society of data-savvy information professionals to enable open leadership innovation
  • Funder: European Commission (EC)
  • Project Code: 693092
  • Funding stream: H2020 | RIA
Validated by funder
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