Introduction
Every day we interact with predictive systems that seek to model our behavior, monitor our activities, and make recommendations: Whom will we befriend? What articles will we like? What products will we purchase? Who influences us in our social network? And do our activities change over time? Models that answer such questions drive important real-world systems, and at the same time are of basic scientific interest to economists, linguists, and social scientists, among others.
Recommender Systems aim to solve tasks such as those above, by learning from large volumes of historical activities to describe the dynamics of user preferences and the properties of the content users interact with. Recommender systems can take many forms (Table 1), though in essence all boil down to modeling the interactions between users and content, in order to predict future actions and preferences. In this chapter, we investigate a few of the most common models and paradigms, starting with...
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McAuley, J. (2022). Recommender Systems. In: Schintler, L.A., McNeely, C.L. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32010-6_516
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DOI: https://doi.org/10.1007/978-3-319-32010-6_516
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