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

TL;DR

This paper presents a methodology for evaluating joint caching and recommendation schemes in real-world content services, demonstrating significant practical performance gains through extensive measurements and user experiments on YouTube.

Contribution

It introduces the first realistic evaluation framework for joint caching and recommendation schemes using real service data and user experiments.

Findings

8 to 10 times increase in cache hit ratio

Significant practical performance gains demonstrated

Validation of previous assumptions and insights for future research

Abstract

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.