@inproceedings{c730c5b09d3a49ca9f6721efe58afa61,
title = "Content recommendation through semantic annotation of user reviews and linked data",
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 theWeb of Data, while it improves in novelty with respect to traditional techniques based on ratings.",
keywords = "DBpedia, Linked Data, Recommender Systems, Semantic Annotation, Semantic Web, User Reviews, Web of Data, Wikidata",
author = "Iacopo Vagliano and Diego Monti and Ansgar Scherp and Maurizio Morisio",
note = "Funding Information: This work was supported by the EU{\textquoteright}s Horizon 2020 programme under grant agreement H2020-693092 MOVING. Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 9th International Conference on Knowledge Capture, K-CAP 2017 ; Conference date: 04-12-2017 Through 06-12-2017",
year = "2017",
month = dec,
day = "4",
doi = "https://doi.org/10.1145/3148011.3148035",
language = "English",
series = "Proceedings of the Knowledge Capture Conference, K-CAP 2017",
publisher = "Association for Computing Machinery, Inc",
booktitle = "Proceedings of the Knowledge Capture Conference, K-CAP 2017",
}