LDA-based term profiles for expert finding in a political setting

Autor: Luis Redondo-Expósito, Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete

Publikováno v: Journal of Intelligent Information Systems. 56:529-559

A common task in many political institutions (i.e. Parliament) is to find politicians who are experts in a particular field. In order to tackle this problem, the first step is to obtain politician profiles which include their interests, and these can

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d225136c088f3e827f887abea75f3c4
https://doi.org/10.1007/s10844-021-00636-x

Positive unlab ele d learning for building recommender systems in a parliamentary setting

Autor: Juan F. Huete, Luis Redondo-Expósito, Luis M. de Campos, Juan M. Fernández-Luna

Publikováno v: Digibug. Repositorio Institucional de la Universidad de Granada
Consejo Superior de Investigaciones Científicas (CSIC)
Digibug: Repositorio Institucional de la Universidad de Granada
Universidad de Granada (UGR)

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 a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb7a95699fc81c179f79448a4b3e2e19

Comparing Machine Learning and Information Retrieval-Based Approaches for Filtering Documents in a Parliamentary Setting

Autor: Luis Redondo-Expósito, Juan M. Fernández-Luna, Luis M. de Campos, Juan F. Huete

Publikováno v: Lecture Notes in Computer Science ISBN: 9783319675817
SUM

We consider the problem of building a content-based recommender/filtering system in a parliamentary context which, given a new document to be recommended, can decide those Members of Parliament who should receive it. We propose and compare two differ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f5c70cf2ff2da96701367761f5b95b46
https://doi.org/10.1007/978-3-319-67582-4_5