Abstract

The recent development of deep learning approaches provides a convenient way to learn entity embeddings from different aspects such as texts and a homogeneous or heterogeneous graph encoded in a knowledge base such as DBpedia. However, it is unclear to what extent domain-specific entity embeddings learned from different aspects of a knowledge base reflect their similarities, and the potential of leveraging those similarities for item recommendations in a specific domain has not been explored. In this work, we investigate domain-specific entity embeddings learned from different aspects of DBpedia with state-of-the-art embedding approaches, and the recommendation performance based on the similarities of these embeddings. The experimental results on two real-word datasets show that recommender systems based on the similarities of entity embeddings learned from a homogeneous graph via the dbo:wikiPageWikiLink property provides the best performance compared to the ones learned from other aspects.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.