@misc{10481/67745,
year = {2018},
url = {http://hdl.handle.net/10481/67745},
abstract = {Our goal is to learn about the political interests and preferences of Members of Parliament (MPs) by mining their parliamentary activity in order to develop a recommendation/filtering system to determine how relevant documents should be distributed among MPs. We propose the use of positive unlabeled learning to tackle this problem since we only have information about relevant documents (the interventions of each MP in debates) but not about irrelevant documents and so it is not possible to use standard binary classifiers which have been trained with positive and negative examples. Additionally, we have also developed a new positive unlabeled learning algorithm that compares favorably with: (a) a baseline approach which assumes that every intervention by any other MP is irrelevant; (b) another well-known positive unlabeled learning method; and (c) an approach based on information retrieval methods that matches documents and legislators’ representations. The experiments have been conducted with data from the regional Spanish Andalusian Parliament.},
organization = {This work has been funded by the Spanish “Ministerio de Economía y Competitividad” under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER).},
publisher = {Elsevier},
keywords = {Recommender system},
keywords = {Positive unlabeled learning},
keywords = {Politician recommendation},
title = {Positive unlab ele d learning for building recommender systems in a parliamentary setting},
doi = {10.1016/j.ins.2017.12.046},
author = {Campos Ibáñez, Luis Miguel and Fernández Luna, Juan Manuel and Huete Guadix, Juan Francisco and Redondo Expósito, Luis},
}