  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.
