A Linked Data browser with recommendations
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Accepted Version
Date
2018-12-17
Authors
Durao, Frederico
Bridge, Derek G.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
It is becoming more common to publish data in a way that accords with the Linked Data principles. In an effort to improve the human exploitation of this data, we propose a Linked Data browser that is enhanced with recommendation functionality. Based on a user profile, also represented as Linked Data, we propose a technique that we call LDRec that chooses in a personalized way which of the resources that lie within a certain neighbourhood in a Linked Data graph to recommend to the user. The recommendation technique, which is novel, is inspired by a collective classifier known as the Iterative Classification Algorithm. We evaluate LDRec using both an off-line experiment and a user trial. In the off-line experiment, we obtain higher hit rates than we obtain using a simpler classifier. In the user trial, comparing against the same simpler classifier, participants report significantly higher levels of overall satisfaction for LDRec.
Description
Keywords
Graph theory , Iterative methods , Linked Data , Online front-ends , Pattern classification , Recommender systems , Semantic Web , User profile , LDRec , Linked Data graph , Recommendation technique , User trial , Linked Data browser , Linked Data principles , Recommendation functionality , Off-line experiment , Iterative classification , Browsers , Resource description framework , Motion pictures , Data models , Tools , Browsing , Recommending , Collective , Classification , Iterative
Citation
Durao, F. and Bridge, D. (2018) 'A Linked Data browser with recommendations', 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, 5-7 November, pp. 189-196. doi:10.1109/ICTAI.2018.00038
Copyright
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