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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors
verfasst von : M. Atif Qureshi, Derek Greene
Erschienen in: Machine Learning and Knowledge Discovery in Databases
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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 extracted as a concept vector through the application of an explainable word embedding technique called EVE. Each dimension of the concept vector is labelled as either a Wikipedia article or a Wikipedia category name, making the vector representation readily interpretable. In order to recommend items, the Lit@EVE system uses these vectors to compute similarity scores between a target novel or author and all other candidate items. Finally, the system generates an ordered list of suggested items by showing the most informative features as human-readable labels, thereby making the recommendation explainable.