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 This work has been supported by the Centre for the Development of Industrial Technology (CDTI) under the funding project CENIT-MIO! CENIT-2008 1019. Mocholi Agües, JA.; Jaén Martínez, FJ.; Krynicki, KK.; Catalá Bolós, A. (2012). TSACO: Extending a context-aware recommendation system with Allen temporal operators. En Ubiquitous Computing and Ambient Intelligence. Springer. 253-260. doi:10.1007/978-3-642-35377-2_35 Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Eng. 17(6), 734–749 (2005) Picón, A., Rodríguez-Vaamonde, S., Jaén, J., Mocholi, J.A., García, D., Cadenas, A.: A statistical recommendation model of mobile services based on contextual evidences. Expert Systems with Applications 39(1), 647–653 (2012) Mocholi, J.A., Jaen, J., Krynicki, K., Catala, A., Picón, A., Cadenas, A.: Learning semantically-annotated routes for context-aware recommendations on map navigation systems. Applied Soft Computing 12(9), 3088–3098 (2012) Linn, Z.Z., Hla, K.H.S.: Temporal Database Queries for Recommender System using Temporal Logic. In: Intl. Symposium on Micro-NanoMechatronics and Human Science, pp. 1–6 (2006) Ullah, F., Sarwar, G., Lee, S.C., Park, Y.K., Moon, K.D., Kim, J.T.: Hybrid recommender system with temporal information. In: Intl. Conf. on Information Networking, pp. 421–425 (2012) Shakshuki, E., Trudel, A., Xu, Y., Li, B.: A Probabilistic Temporal Interval Algebra Based Multi-agent Scheduling System. In: International Joint Conference on Artificial Intelligence Workshop in Multi-Agent Information Retrieval and Recommender Systems, pp. 62–69 (2005) Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983) Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Trans. Systems, Man and Cybernetics, Part B 26, 29–34 (1996) Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: Algorithms, applications and advances. In: Glover, F., Kochen-berger, G. (eds.) Handbook of Metaheuristics, pp. 251–285. Kluwer Academic Publishers (2003) Khan, L., McLeod, D.: Audio structuring and personalized retrieval using ontologies. IEEE Advances in Digital Libraries (2000) Liang, Y.C., Smith, A.E.: An Ant Colony Approach to the Orienteering Problem. Journal of the Chinese Institute of Industrial Engineers 23(5), 403–414 (2003)

TSACO: Extending a context-aware recommendation system with Allen temporal operators

Abstract