Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning

Saurabh Paul

Abstract:

The issue of fairness in recommendation is becoming increasingly
essential as Recommender Systems (RS) touch and influence more
and more people in their daily lives. In fairness-aware recommenda-
tion, most of the existing algorithmic approaches mainly aim at solv-
ing a constrained optimization problem by imposing a constraint
on the level of fairness while optimizing the main recommendation
objective, e.g., click through rate (CTR). While this alleviates the im-
pact of unfair recommendations, the expected return of an approach
may significantly compromise the recommendation accuracy due to
the inherent trade-off between fairness and utility. This motivates
us to deal with these conflicting objectives and explore the optimal
trade-off between them in recommendation. One conspicuous ap-
proach is to seek a Pareto efficient/optimal solution to guarantee
optimal compromises between utility and fairness. Moreover, con-
sidering the needs of real-world e-commerce platforms, it would be
more desirable if we can generalize the whole Pareto Frontier, so that
the decision-makers can specify any preference of one objective
over another based on their current business needs. Therefore, in
this work, we propose a fairness-aware recommendation framework
using multi-objective reinforcement learning (MORL), called MoFIR
(pronounced “more fair”), which is able to learn a single paramet-
ric representation for optimal recommendation policies over the
space of all possible preferences. Specially, we modify traditional
Deep Deterministic Policy Gradient (DDPG) by introducing condi-
tioned network (CN) into it, which conditions the networks directly
on these preferences and outputs Q-value-vectors. Experiments on
several real-world recommendation datasets verify the superiority
of our framework on both fairness metrics and recommendation
measures when compared with all other baselines. We also extract
the approximate Pareto Frontier on real-world datasets generated
by MoFIR and compare to state-of-the-art fairness methods.