Producer-Fairness in Sequential Bundle Recommendation
This addresses fairness for producers in recommendation systems, but it is incremental as it adapts existing methods to bundle contexts.
The paper tackles fairness in sequential bundle recommendation by formalizing producer-fairness to ensure desired exposure for item groups across users, and proposes exact and heuristic solutions that maintain bundle quality in experiments on real-world datasets.
We address fairness in the context of sequential bundle recommendation, where users are served in turn with sets of relevant and compatible items. Motivated by real-world scenarios, we formalize producer-fairness, that seeks to achieve desired exposure of different item groups across users in a recommendation session. Our formulation combines naturally with building high quality bundles. Our problem is solved in real time as users arrive. We propose an exact solution that caters to small instances of our problem. We then examine two heuristics, quality-first and fairness-first, and an adaptive variant that determines on-the-fly the right balance between bundle fairness and quality. Our experiments on three real-world datasets underscore the strengths and limitations of each solution and demonstrate their efficacy in providing fair bundle recommendations without compromising bundle quality.