Lit@EVE: Explainable Recommendation based on Wikipedia Concept Vectors

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Title: Lit@EVE: Explainable Recommendation based on Wikipedia Concept Vectors
Authors: Qureshi, M. AtifGreene, Derek
Permanent link: http://hdl.handle.net/10197/9055
Date: 30-Dec-2017
Online since: 2017-11-28T12:57:19Z
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
Funding Details: Insight Research Centre
Type of material: Conference Publication
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science
Copyright (published version): 2017 Springer
Keywords: Machine learningStatistics
Other versions: http://ecmlpkdd2017.ijs.si/index.html
Language: en
Status of Item: Peer reviewed
Is part of: Altun Y. et al. (eds.). Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, vol 10536
Conference Details: The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Skopje, Macedonia 18-22 September
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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