Abstract
We consider the problem of building a content-based recommender system in a parliamentary context, which may be used for two different but related tasks. First, we consider a filtering task where, given a new document to be recommended, the system can decide those Members of the Parliament who should receive it. Second, we also consider a recommendation task where, given a request from a citizen, the system should present information on those deputies that are more involved in the topics of the request. To build the system we collected, for each Member of the Parliament, the text of corresponding speeches within the parliament debates and generated, with different techniques, a profile that was used to match against the input (document or request). We tested our methods using the documents of the regional Andalusian Parliament at Spain, obtaining promising results.
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Notes
- 1.
An initiative is the literal transcription of the discussion in the parliament of a petition presented by specific MPs or groups.
- 2.
- 3.
In parentheses we present the short acronym used in the figures and tables in this section.
- 4.
- 5.
We consider that this is a rather conservative assumption, because it is quite reasonable to think that an initiative can also be relevant to other MPs.
- 6.
The change in the underlying intent between the original query and its expanded form.
- 7.
We are not going to discuss their impact in this work, since the accuracy values obtained are worse in all the cases.
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Acknowledgements
Paper supported by the Spanish “Ministerio de Economía y Competitividad” under the project TIN2013-42741-P.
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de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Calado, P., Martins, B. (2015). Learning Parliamentary Profiles for Recommendation Tasks. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_17
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