  Social commerce platforms are emerging businesses where producers sell
products through re-sellers who advertise the products to other customers in
their social network. Due to the increasing popularity of this business model,
thousands of small producers and re-sellers are starting to depend on these
platforms for their livelihood; thus, it is important to provide fair earning
opportunities to them. The enormous product space in such platforms prohibits
manual search, and motivates the need for recommendation algorithms to
effectively allocate product exposure and, consequently, earning opportunities.
In this work, we focus on the fairness of such allocations in social commerce
platforms and formulate the problem of assigning products to re-sellers as a
fair division problem with indivisible items under two-sided cardinality
constraints, wherein each product must be given to at least a certain number of
re-sellers and each re-seller must get a certain number of products.
  Our work systematically explores various well-studied benchmarks of fairness
-- including Nash social welfare, envy-freeness up to one item (EF1), and
equitability up to one item (EQ1) -- from both theoretical and experimental
perspectives. We find that the existential and computational guarantees of
these concepts known from the unconstrained setting do not extend to our
constrained model. To address this limitation, we develop a mixed-integer
linear program and other scalable heuristics that provide near-optimal
approximation of Nash social welfare in simulated and real social commerce
datasets. Overall, our work takes the first step towards achieving provable
fairness alongside reasonable revenue guarantees on social commerce platforms.
