Providing personalized offers, and services in general, for the users of a system requires perceiving the context in which the users’ preferences are rooted. Accordingly, context modeling is becoming a relevant issue and an expanding research field. Moreover, the frequent changes of context may induce a change in the current preferences; thus, appropriate learning methods should be employed for the system to adapt automatically. In this work, we introduce a methodology based on the so-called Context Dimension Tree—a model for representing the possible contexts in the very first stages of Application Design—as well as an appropriate conceptual architecture to build a recommender system for travelers.
CITATION STYLE
Javadian Sabet, A., Rossi, M., Schreiber, F. A., & Tanca, L. (2021). Towards learning travelers’ preferences in a context-aware fashion. In Advances in Intelligent Systems and Computing (Vol. 1239 AISC, pp. 203–212). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58356-9_20
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