Fairness in Network-Friendly Recommendations

TL;DR

This paper investigates the fairness issues in network-friendly recommendations (NFR), highlighting the unfairness caused by existing schemes and proposing a linear optimization-based method to balance network performance with fairness guarantees.

Contribution

It is the first study to analyze fairness in NFR, quantify the unfairness, and develop a fair-NFR framework using linear optimization to improve fairness without sacrificing network gains.

Findings

NFR can cause significant unfairness among content providers.

There is an inherent trade-off between network gains and fairness in NFR.

Fair-NFR achieves high network performance with minimal unfairness.

Abstract

As mobile traffic is dominated by content services (e.g., video), which typically use recommendation systems, the paradigm of network-friendly recommendations (NFR) has been proposed recently to boost the network performance by promoting content that can be efficiently delivered (e.g., cached at the edge). NFR increase the network performance, however, at the cost of being unfair towards certain contents when compared to the standard recommendations. This unfairness is a side effect of NFR that has not been studied in literature. Nevertheless, retaining fairness among contents is a key operational requirement for content providers. This paper is the first to study the fairness in NFR, and design fair-NFR. Specifically, we use a set of metrics that capture different notions of fairness, and study the unfairness created by existing NFR schemes. Our analysis reveals that NFR can be significantly unfair. We identify an inherent trade-off between the network gains achieved by NFR and the resulting unfairness, and derive bounds for this trade-off. We show that existing NFR schemes frequently operate far from the bounds, i.e., there is room for improvement. To this end, we formulate the design of Fair-NFR (i.e., NFR with fairness guarantees compared to the baseline recommendations) as a linear optimization problem. Our results show that the Fair-NFR can achieve high network gains (similar to non-fair-NFR) with little unfairness.