Lit@EVE: Explainable Recommendation based on Wikipedia Concept Vectors
|Title:||Lit@EVE: Explainable Recommendation based on Wikipedia Concept Vectors||Authors:||Qureshi, M. Atif
|Permanent link:||http://hdl.handle.net/10197/9055||Date:||22-Sep-2017||Abstract:||We 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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Keywords:||Machine learning;Statistics||Language:||en||Status of Item:||Peer reviewed||Conference Details:||The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases, Skopje, Macedonia 18-22 September|
|Appears in Collections:||Insight Research Collection|
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