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LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops

Chen Xu, Xiaopeng Ye, Jun Xu, Xiao Zhang, Weiran Shen, Ji-Rong Wen

TL;DR

This work tackles long-term provider fairness in multi-stakeholder recommender systems under recommendation feedback loops by introducing LTP-MMF, an online ranking model that blends matrix-factorization accuracy with a dual MMF-based fairness module and UCB-driven exploration. Framed as batched context-bandits, the method optimizes the objective $R=\frac{1}{T}\sum_t g(\\mathbf{x}_t) + \lambda r(\\mathbf{e})$ with exponential emphasis on fairness to uplift worst-off providers while maintaining user satisfaction. The approach yields sub-linear regret bounds and demonstrates superior long-term performance on four public datasets, with notable Pareto improvements and efficient online inference. Overall, LTP-MMF offers a scalable, fair, and effective framework for balancing long-term provider exposure and user utility in dynamic recommendation settings.

Abstract

Multi-stakeholder recommender systems involve various roles, such as users, and providers. Previous work pointed out that max-min fairness (MMF) is a better metric to support weak providers. However, when considering MMF, the features or parameters of these roles vary over time, how to ensure long-term provider MMF has become a significant challenge. We observed that recommendation feedback loops (named RFL) will greatly influence the provider MMF in the long term. RFL means that recommender systems can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback. When utilizing the feedback, the recommender model will regard the unexposed items as negative. In this way, the tail provider will not get the opportunity to be exposed, and its items will always be considered negative samples. Such phenomena will become more and more serious in RFL. To alleviate the problem, this paper proposes an online ranking model named Long-Term Provider Max-min Fairness (named LTP-MMF). Theoretical analysis shows that the long-term regret of LTP-MMF enjoys a sub-linear bound. Experimental results on three public recommendation benchmarks demonstrated that LTP-MMF can outperform the baselines in the long term.

LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops

TL;DR

This work tackles long-term provider fairness in multi-stakeholder recommender systems under recommendation feedback loops by introducing LTP-MMF, an online ranking model that blends matrix-factorization accuracy with a dual MMF-based fairness module and UCB-driven exploration. Framed as batched context-bandits, the method optimizes the objective with exponential emphasis on fairness to uplift worst-off providers while maintaining user satisfaction. The approach yields sub-linear regret bounds and demonstrates superior long-term performance on four public datasets, with notable Pareto improvements and efficient online inference. Overall, LTP-MMF offers a scalable, fair, and effective framework for balancing long-term provider exposure and user utility in dynamic recommendation settings.

Abstract

Multi-stakeholder recommender systems involve various roles, such as users, and providers. Previous work pointed out that max-min fairness (MMF) is a better metric to support weak providers. However, when considering MMF, the features or parameters of these roles vary over time, how to ensure long-term provider MMF has become a significant challenge. We observed that recommendation feedback loops (named RFL) will greatly influence the provider MMF in the long term. RFL means that recommender systems can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback. When utilizing the feedback, the recommender model will regard the unexposed items as negative. In this way, the tail provider will not get the opportunity to be exposed, and its items will always be considered negative samples. Such phenomena will become more and more serious in RFL. To alleviate the problem, this paper proposes an online ranking model named Long-Term Provider Max-min Fairness (named LTP-MMF). Theoretical analysis shows that the long-term regret of LTP-MMF enjoys a sub-linear bound. Experimental results on three public recommendation benchmarks demonstrated that LTP-MMF can outperform the baselines in the long term.
Paper Structure (34 sections, 4 theorems, 24 equations, 9 figures, 3 tables, 1 algorithm)

This paper contains 34 sections, 4 theorems, 24 equations, 9 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Formulation
  4. Multi-Stakeholders Recommender Systems
  5. Amortized Provider Max-Min Fairness
  6. Bandit with Provider Fairness
  7. Our approach: LTP-MMF
  8. Overall Architectures
  9. Accuracy Module: Matrix Factorization
  10. Fairness Module
  11. UCB for Accuracy-fairness Reward
  12. Our Algorithm: LTP-MMF
  13. Discussion
  14. Experiment
  15. Experimental settings
  16. ...and 19 more sections

Key Result

Theorem 1

The dual problem of Equation eq:solve_OPT can be written as: where $\bm{M}\in\mathbb{R}^{|\mathcal{I}|\times|\mathcal{P}|}$ is the item-provider adjacency matrix with $M_{ip} = 1$ indicating item $i\in \mathcal{I}_p$, $g^*(\cdot),r^*(\cdot)$ the conjugate functions: with $\mathcal{X} = \{\mathbf{x}_t\mid\mathbf{x}_t \in \{0,1\} \land \sum_{i\in\mathcal{I}} \mathbf{x}_{ti} = K\}$, and $\mathcal{D

Figures (9)

  • Figure 1: \ref{['fig:intro_loop']} The feedback loops of the interaction between the fairness model and users.\ref{['fig:intro_simulation']}Simulations of the long-term lowest exposures among all provider (abbreviated as Lowest Exposures)
  • Figure 2: Sequential item ranking process of LTP-MMF
  • Figure 3: Pareto frontier in Four Dataset
  • Figure 4: Exploration Weight
  • Figure 5: Ablation study for the exploration of LTP-MMF.
  • ...and 4 more figures

Theorems & Definitions (8)

  • Theorem 1: Dual Problem
  • Lemma 1
  • Remark 1
  • Theorem 2: Confidence Radius of Ranking
  • Remark 2
  • Theorem 3: Regret Bound
  • Remark 3: Accuracy-fairness regret trade-off
  • Remark 4: Sublinear Long-term Regret