@inproceedings{458f21593ef247ed8cf5fea753f79f62,
title = "Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors",
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.",
author = "Qureshi, {M. Atif} and Derek Greene",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 ; Conference date: 18-09-2017 Through 22-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71273-4_41",
language = "English",
isbn = "9783319712727",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "409--413",
editor = "Michelangelo Ceci and Saso Dzeroski and Donato Malerba and Yasemin Altun and Kamalika Das and Jesse Read and Marinka Zitnik and Jerzy Stefanowski and Taneli Mielik{\"a}inen",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings",
address = "Germany",
}