3 research outputs found
Positive unlab ele d learning for building recommender systems in a parliamentary setting
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.This work has been funded by the Spanish “Ministerio de EconomĂa y Competitividad” under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)
Positive unlabeled learning for building recommender systems in a parliamentary setting
Our goal is to learn about the political interests and preferences of the
Members of Parliament by mining their parliamentary activity, in order to
develop a recommendation/filtering system that, given a stream of documents to
be distributed among them, is able to decide which documents should receive
each Member of Parliament. We propose to use positive unlabeled learning to
tackle this problem, because we only have information about relevant documents
(the own interventions of each Member of Parliament in the debates) but not
about irrelevant documents, so that we cannot use standard binary classifiers
trained with positive and negative examples. We have also developed a new
algorithm of this type, which compares favourably with: a) the baseline
approach assuming that all the interventions of other Members of Parliament are
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 carried out with data
from the regional Andalusian Parliament at Spain