Producer-Fairness in Sequential Bundle Recommendation

TL;DR

This paper introduces a formal framework for producer-fairness in sequential bundle recommendation, balancing fairness and quality in real-time recommendations, with solutions tested on real datasets.

Contribution

It formalizes producer-fairness in sequential bundle recommendation and proposes exact and heuristic solutions, including an adaptive method balancing fairness and quality.

Findings

Heuristic methods perform well in real-world scenarios.

Adaptive approach effectively balances fairness and quality.

Experimental results validate the proposed methods' efficacy.

Abstract

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.