By Enrique Machuca, Lawrence Mandow, Lucie Galand (auth.), Concha Bielza, Antonio Salmerón, Amparo Alonso-Betanzos, J. Ignacio Hidalgo, Luis Martínez, Alicia Troncoso, Emilio Corchado, Juan M. Corchado (eds.)
This booklet constitutes the refereed complaints of the fifteenth convention of the Spanish organization for man made Intelligence, CAEPIA 20013, held in Madrid, Spain, in September 2013. The 27 revised complete papers awarded have been conscientiously chosen from sixty six submissions. The papers are geared up in topical sections on Constraints, seek and making plans, clever internet and knowledge retrieval, fuzzy platforms, wisdom illustration, reasoning and common sense, laptop studying, multiagent structures, multidisciplinary issues and purposes, metaheuristics, uncertainty in man made intelligence.
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Extra info for Advances in Artificial Intelligence: 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013. Proceedings
This method computes probability distribution functions, which represent the probabilities that users are associated to particular events, and uses computed probabilities to assign a score to each user in a household given a new event. More specifically, we compute the probability mass function (PMF) of each feature given a particular user, restricted to the information related with that user’s household, that is, | , is the set of users in the household . Then, for each new event , we where obtain its representation as a feature vector , and identify the user who maximizes arg max the PMF, that is, | .
Most successful approaches search for documents that are both opinionated and on-topic by considering positional information as the best guidance to ﬁnd on-topic opinions [3,4]. Pseudo Relevance Feedback (PRF) combined with external opinion resources has also been proposed to support opinion ﬁnding . However, most studies ignore the structural aspects of a blog post to determine opinions. This is unfortunate because sentiments often appear in speciﬁc locations of the text. For instance, in the study of blog comments presented in , Mishne and Glance found that comments constitute a substantial part of the blogosphere, accounting for up to 30% of the total volume of blog data.
The first strategy is to increase the diversity of delivered recommendations , aiming to cover the heterogeneous range of preferences of the different members in a household. The second strategy is to identify the active household members for which recommendations have to be delivered. In this paper, we focus on the second strategy since it lets make more accurate recommendations, by only using preferences of active members, and discarding preferences of other, non-present members . Previous work on the task has shown that the analysis of temporal patterns on historical data of household accounts provides important information for the discrimination of users, letting accurately identify active members [3,5,6].
Advances in Artificial Intelligence: 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013. Proceedings by Enrique Machuca, Lawrence Mandow, Lucie Galand (auth.), Concha Bielza, Antonio Salmerón, Amparo Alonso-Betanzos, J. Ignacio Hidalgo, Luis Martínez, Alicia Troncoso, Emilio Corchado, Juan M. Corchado (eds.)