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Network-aware Recommendations in the Wild: Methodology, Realistic Evaluations, Experiments

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Savvas Kastanakis
  • Pavlos Sermpezis
  • Vasileios Kotronis
  • Daniel Menasché
  • Thrasyvoulos Spyropoulos
Article number7
<mark>Journal publication date</mark>1/07/2022
<mark>Journal</mark>IEEE Transactions on Mobile Computing
Issue number7
Number of pages14
Pages (from-to)2466-2479
Publication StatusPublished
Early online date4/12/20
<mark>Original language</mark>English


Joint caching and recommendation has been recently proposed as a new paradigm for increasing the efficiency of mobile edge caching. Early findings demonstrate significant gains for the network performance. However, previous works evaluated the proposed schemes exclusively on simulation environments. Hence, it still remains uncertain whether the claimed benefits would change in real settings. In this paper, we propose a methodology that enables to evaluate joint network and recommendation schemes in real content services by only using publicly available information. We apply our methodology to the YouTube service, and conduct extensive measurements to investigate the potential performance gains. Our results show that significant gains can be achieved in practice; e.g., 8 to 10 times increase in the cache hit ratio from cache-aware recommendations. Finally, we build an experimental testbed and conduct experiments with real users; we make available our code and datasets to facilitate further research. To our best knowledge, this is the first realistic evaluation (over a real service, with real measurements and user experiments) of the joint caching and recommendations paradigm. Our findings provide experimental evidence for the feasibility and benefits of this paradigm, validate assumptions of previous works, and provide insights that can drive future research.