Content recommendation through semantic annotation of user reviews and linked data

Citation

Vagliano I, Monti D, Scherp A & Morisio M (2017) Content recommendation through semantic annotation of user reviews and linked data. In: Proceedings of the Knowledge Capture Conference. Knowledge Capture Conference K-Cap 2017, Austin, TX, USA, 04.12.2017-06.12.2017. New York: ACM, p. Article 32. https://doi.org/10.1145/3148011.3148035

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.

Keywords
Recommender systems; user reviews; semantic annotation; linked data; web of data; semantic web; DBpedia; Wikidata;

Journal
Proceedings of the Knowledge Capture Conference, K-CAP 2017