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.
Cite
Text
Qureshi and Greene. "Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_41Markdown
[Qureshi and Greene. "Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/qureshi2017ecmlpkdd-lit/) doi:10.1007/978-3-319-71273-4_41BibTeX
@inproceedings{qureshi2017ecmlpkdd-lit,
title = {{Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors}},
author = {Qureshi, Muhammad Atif and Greene, Derek},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {409-413},
doi = {10.1007/978-3-319-71273-4_41},
url = {https://mlanthology.org/ecmlpkdd/2017/qureshi2017ecmlpkdd-lit/}
}