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. Atif; Greene, 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 learning; Statistics | 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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