Lit@EVE: Explainable Recommendation based on Wikipedia Concept Vectors

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dc.contributor.authorQureshi, M. Atif-
dc.contributor.authorGreene, Derek- Springeren_US
dc.descriptionThe European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Skopje, Macedonia 18-22 Septemberen_US
dc.description.abstractWe present an explainable recommendation system for novels and authors,called Lit@EVE, which is based on Wikipedia concept vectors. In this system,each novel or author is treated as a concept whose definition is extractedas a concept vector through the application of an explainable word embeddingtechnique called EVE. Each dimension of the concept vector is labelled as eithera Wikipedia article or a Wikipedia category name, making the vector representationreadily interpretable. In order to recommend items, the Lit@EVE systemuses these vectors to compute similarity scores between a target novel or authorand all other candidate items. Finally, the system generates an ordered list of suggesteditems by showing the most informative features as human-readable labels,thereby making the recommendation explainable.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.relation.ispartofAltun Y. et al. (eds.). Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, vol 10536en_US
dc.relation.ispartofseriesLecture Notes in Computer Scienceen_US
dc.rightsThe final publication is available at Springer via
dc.subjectMachine learningen_US
dc.titleLit@EVE: Explainable Recommendation based on Wikipedia Concept Vectorsen_US
dc.typeConference Publicationen_US
dc.statusPeer revieweden_US
dc.neeo.contributorQureshi|M. Atif|aut|-
dc.description.othersponsorshipInsight Research Centreen_US
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