Exploiting FrameNet for Content-Based Book Recommendation
Orphée De Clercq, Michael Schuhmacher, Simone Paolo Ponzetto, Véronique Hoste · MADOC (University of Mannheim) · 2014
Adding semantic knowledge to a content-based recommender helps to better understand the items and user representations.Most recent research has focused on examining the added value of adding semantic features based on structured web data, in particular Linked Open Data (LOD).In this paper, we focus in contrast on semantic feature construction from text, by incorporating features based on semantic frames into a book recommendation classifier.To this purpose we leverage the semantic frames based on parsing the plots of the items under consideration with a state of the art semantic parser.By investigating this type of semantic information, we show that these frames are also able to represent information about a particular book, but without the need of having explicitly structured data describing the books available.We reveal that exploiting frame information outperforms a basic bag-of-words approach and that especially the words relating to those frames are beneficial for classification.In a final step we compare and combine our system with the LOD features from a system leveraging DBpedia as knowledge resource.We show that both approaches yield similar results and reveal that combining semantic information from these two different sources might even be beneficial.