Abstract
In this paper we present our research to extend a recommender system based on a semantic multicriteria ant colony algorithm to allow the use of Allen temporal operators. The system utilizes user’s learnt routes, including their associated context information, in order to predict the most likely route a user is following, given his current location and context data. The addition of temporal operators will increase the level of expressiveness of the queries the system can answer what will allow, in turn, more fine-tuned predictions. This more refined knowledge could then be used as the basis for offering services related to his current (or most likely future) context in the vicinity of the path the user is following.
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Mocholi, J.A., Jaen, J., Krynicki, K., Catala, A. (2012). TSACO: Extending a Context-Aware Recommendation System with Allen Temporal Operators. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2012. Lecture Notes in Computer Science, vol 7656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35377-2_35
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DOI: https://doi.org/10.1007/978-3-642-35377-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35376-5
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