  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.
