Abstract
Recommender systems (RS) are increasingly adopted by e-business, social networks and many other user-centric websites. Based on the user’s previous choices or interests, a RS suggests new items in which the user might be interested. With constant changes in user behavior, the quality of a RS may decrease over time. Therefore, we need to monitor the performance of the RS, giving timely information to management, who can than manage the RS to maximize results. Our work consists in creating a monitoring platform - based on Business Intelligence (BI) and On-line Analytical Processing (OLAP) tools - that provides information about the recommender system, in order to assess its quality, the impact it has on users and their adherence to the recommendations. We present a case study with Palco Principal, a social network for music.
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Félix, C., Soares, C., Jorge, A., Vinagre, J. (2014). Monitoring Recommender Systems: A Business Intelligence Approach. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_21
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DOI: https://doi.org/10.1007/978-3-319-09153-2_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09152-5
Online ISBN: 978-3-319-09153-2
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