Qureshi, M. AtifM. AtifQureshiGreene, DerekDerekGreene2017-11-282017-11-282017 Sprin2017-12-30http://hdl.handle.net/10197/9055The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Skopje, Macedonia 18-22 SeptemberWe 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.enThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-71273-4_41Machine learningStatisticsConference Publication2017-07-06https://creativecommons.org/licenses/by-nc-nd/3.0/ie/