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||Other versions:||http://ecmlpkdd2017.ijs.si/index.html||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|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.