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Recommendation as a Stochastic Sequential Decision Problem

By Ronen I. Brafman, David Heckerman and Guy Shani


Recommender systems -- systems that suggest to users in e-commerce sites items that might interest them -- adopt a static view of the recommendation process and treat it as a prediction problem. In an earlier paper, we argued that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDPs) provide a more appropriate model for recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation, and they take into account the expected value of each recommendation. The use of MDPs in a commercial site raises three fundamental problems: providing an adequate initial model, updating this model online as new items (e.g., books) arrive, and coping with the enormous state-space of this model. In past work, we dealt with the first problem

Year: 2003
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