Bridging the gap between linked open data-based recommender systems and distributed representations. (December 2019) - Basile, Pierpaolo; Greco, Claudio; Suglia, Alessandro; Semeraro, Giovanni
Abstract: Recently, several methods have been proposed for introducing Linked Open Data (LOD) into recommender systems. LOD can be used to enrich the representation of items by leveraging RDF statements and adopting graph-based methods to implement effective recommender systems. However, most of those methods do not exploit embeddings of entities and relations built on knowledge graphs, such as datasets coming from the LOD. In this paper, we propose a novel recommender system based on holographic embeddings of knowledge graphs built from Wikidata, a free and open knowledge base that can be read and edited by both humans and machines. The evaluation performed on three standard datasets such as Movielens 1M, Last.fm and LibraryThing shows promising results, which confirm the effectiveness of the proposed method. Highlights: We introduce Linked Open Data (LOD) into a content-based recommender system. LOD are used to enrich the representation of items by leveraging RDF statements. LOD are introduced through holographic embeddings of knowledge graphs built from Wikidata. Evaluation performed on three standard datasets: Movielens 1M, Last.fm and LibraryThing. The experimental results are promising and confirm the effectiveness of the proposed method.