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Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report

Vagliano, Iacopo and Monti, Diego and Scherp, Ansgar and Morisio, Maurizio Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report. UNSPECIFIED. UNSPECIFIED.

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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. Additionally, our method achieved a better performance with Wikidata than DBpedia.

Item Type: Monograph (UNSPECIFIED)
Additional Information: Ninth International Conference on Knowledge Capture
Uncontrolled Keywords: cs.IR, cs.IR
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 31 Jul 2019 11:50
Last Modified: 31 Jul 2019 12:15
URI: http://repository.essex.ac.uk/id/eprint/24368

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