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Joint modeling and optimization of caching and recommendation systems

Abstract : Caching content closer to the users has been proposed as a win-win scenario in order to offer better rates to the users while saving costs from the operators. Nonetheless, caching can be successful if the cached files manage to attract a lot of requests. To this end, we take advantage of the fact that the internet is becoming more entertainment oriented and propose to bind recommendation systems and caching in order to increase the hit rate. We model a user who requests multiple contents from a network which is equipped with a cache. We propose a modeling framework for such a user which is based on Markov chains and depart from the IRM. We delve into different versions of the problem and derive optimal and suboptimal solutions according to the case we examine. Finally we examine the variation of the Recommendation aware caching problem and propose practical algorithms that come with performance guarantees. For the former, the results indicate that there are high gains for the operators and that myopic schemes without a vision, are heavily suboptimal. While for the latter, we conclude that the caching decisions can significantly improve when taking into consideration the underlying recommendations.
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Submitted on : Monday, January 3, 2022 - 4:32:07 PM
Last modification on : Wednesday, January 5, 2022 - 3:47:50 AM
Long-term archiving on: : Monday, April 4, 2022 - 8:46:46 PM


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  • HAL Id : tel-03508454, version 1


Theodoros Giannakas. Joint modeling and optimization of caching and recommendation systems. Networking and Internet Architecture [cs.NI]. Sorbonne Université, 2020. English. ⟨NNT : 2020SORUS317⟩. ⟨tel-03508454⟩



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