Abstract
This work investigates the use of Time-Aware Recommender Systems in e-learning systems. In this sense, in the work are defined recommender systems architectures taking into account how the time can be used in recommender systems in the learning domain. For each architecture the main requirements to use the time in a specific way is identified, and some algorithm ideas area presented. Scenarios are presented to illustrate how the proposal architectures can be useful. The results of this work can guide other researches on the field to apply recommender systems techniques in the learning domain.
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de Borba, E.J., Gasparini, I., Lichtnow, D. (2018). Describing Scenarios and Architectures for Time-Aware Recommender Systems for Learning. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-93375-7_17
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