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Caching content over CDNs or at the network edge has been solidified as ameans to improve network cost and offer better streaming experience to users.Furthermore, nudging the users towards low-cost content has recently gainedmomentum as a strategy to boost network performance. We focus on the problem ofoptimal policy design for Network Friendly Recommendations (NFR). We departfrom recent modeling attempts, and propose a Markov Decision Process (MDP)formulation. MDPs offer a unified framework that can model a user with randomsession length. As it turns out, many state-of-the-art approaches can be castas subcases of our MDP formulation. Moreover, the approach offers flexibilityto model users who are reactive to the quality of the received recommendations.In terms of performance, for users consuming an arbitrary number of contents insequence, we show theoretically and using extensive validation over real tracesthat the MDP approach outperforms myopic algorithms both in session cost aswell as in offered recommendation quality. Finally, even compared to optimalstate-of-art algorithms targeting specific subcases, our MDP framework issignificantly more efficient, speeding the execution time by a factor of 10,and enjoying better scaling with the content catalog and recommendation batchsizes.

SOBA: Session optimal MDP-based network friendly recommendations