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Comparing monolithic and committee-based profiles for politician recommendation

Published: 14 June 2016 Publication History

Abstract

In a parliamentary setting, citizen could be interested in knowing those Members of Parliament (MPs) who are working in different areas or involved in the resolution of some people's problems. These topics of interest are usually represented by means of a profile. In this paper, the politicians' profiles are built considering the speeches in parliamentary sessions. However, in most of the cases a single profile is not the best alternative to represent MPs' interests because the specific terms related to a given topic are mixed with others, so that the MPs' preferences are diluted. The alternative is to build different subprofiles containing each one the most representative keywords for each topic, creating in this way a richer representation. We present a first approach to build subprofiles based on the MPs' speeches in different committee and plenary sessions, which will be compared, in terms of performance, to monolithic profiles for an MP content-based recommendation task.

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cover image ACM Other conferences
CERI '16: Proceedings of the 4th Spanish Conference on Information Retrieval
June 2016
146 pages
ISBN:9781450341417
DOI:10.1145/2934732
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • University of Granada: University of Granada

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2016

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Author Tags

  1. Content-based Recommendation
  2. Information Retrieval
  3. Members of Parliaments
  4. Profiles
  5. Subprofiles

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  • Research-article
  • Research
  • Refereed limited

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CERI '16

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CERI '16 Paper Acceptance Rate 18 of 27 submissions, 67%;
Overall Acceptance Rate 36 of 51 submissions, 71%

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