Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report
This work addresses the need for more effective recommender systems in social and e-commerce domains by leveraging textual reviews and linked data, though it appears incremental as it builds on existing semantic and data-driven approaches.
The paper tackles the problem of content recommendation by using semantic annotation of user reviews and linked data from DBpedia and Wikidata to extract non-trivial item information, resulting in better ranking than another Web of Data-based method and improved novelty over traditional rating-based techniques, with Wikidata performing better than DBpedia.
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.