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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.)

ISBN-10: 3642406424

ISBN-13: 9783642406423

This booklet constitutes the refereed court cases 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 provided have been conscientiously chosen from sixty six submissions. The papers are equipped in topical sections on Constraints, seek and making plans, clever net and knowledge retrieval, fuzzy structures, wisdom illustration, reasoning and common sense, desktop studying, multiagent structures, multidisciplinary themes 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

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7295, pp. 437–452. Springer, Heidelberg (2012) 11. : Querying RDF Dictionaries in Compressed Space. ACM SIGAPP Applied Computing Reviews 12(2), 64–77 (2012) 12. , Seaborne, A. ): SPARQL Query Language for RDF. org/TR/rdf-sparql-query/ 13. : Linked data and multimedia: the state of affairs. Multimedia Tools Appl. 59(2), 523–556 (2012) 14. : Managing Gigabytes: Compressing and Indexing Documents and Images. Morgan Kaufmann (1999) Time-Aware Evaluation of Methods for Identifying Active Household Members in Recommender Systems Pedro G.

Time-Aware Evaluation of Methods for Identifying Active Household Members 31 References 1. : Modeling Multiple Users’ Purchase over a Single Account for Collaborative Filtering. , Suel, T. ) WISE 2010. LNCS, vol. 6488, pp. 328–341. Springer, Heidelberg (2010) 2. : Challenge on Context-Aware Movie Recommendation: CAMRa2011. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 385–386 (2011) 3. : Time feature Selection for Identifying Active Household Members. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp.

Time-Aware Evaluation of Methods for Identifying Active Household Members 29 We also observe in Table 1 that the discrimination power of the different time features varies among methodologies. In the case of the A priori method, the best results on time-independent methodologies and are obtained with hour of the day (H) feature, while absolute date (D) achieves the best results among ML methods –we note that results are similar across features. However, when using the stricter , the best results among methods are obtained with day of the week (W) feature, nearly followed by the hour of the day feature.

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