Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem

• URL: http://arxiv.org/abs/2604.21750v1
• Date: Thu, 23 Apr 2026 14:59:54 GMT
• Title: Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem
• Abstract summary: This paper explores the implications of algorithmic pluralism (also known as "middleware" in the governance literature) in which recommendation algorithms are decoupled from platforms.<n>We examine how data portability scenarios produce varying effects on user utility across different recommendation algorithms.
• Score: 0.7711381100220001
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optimizing outcomes for multiple stakeholders in recommender systems has historically focused on algorithmic interventions, such as developing multi-objective models or re-ranking results from existing algorithms. However, structural changes to the recommendation ecosystem itself remain understudied. This paper explores the implications of algorithmic pluralism (also known as "middleware" in the governance literature), in which recommendation algorithms are decoupled from platforms, enabling users to select their preferred algorithm. Prior simulation work demonstrates that algorithmic choice benefits niche consumers and providers. Yet this approach raises critical questions about user modeling in the context of data portability: when users switch algorithms, what happens to their data? Noting that multiple data portability regulations have emerged to strengthen user data ownership and control. We examine how such policies affect user models and stakeholders' outcomes in recommendation setting. Our findings reveal that data portability scenarios produce varying effects on user utility across different recommendation algorithms. We highlight key policy considerations and implications for designing equitable recommendation ecosystems.

Related papers

• Tree of Preferences for Diversified Recommendation [54.183647833064136]
  We study diversified recommendation from a data-bias perspective.<n>Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach.
  arXiv  Detail & Related papers  (2025-12-24T04:13:17Z)
• Fairness for niche users and providers: algorithmic choice and profile portability [0.7711381100220001]
  We study the impact of algorithmic pluralism on the recommendation ecosystem.<n>Prior work has shown that niche consumers and (especially) niche providers benefit from algorithmic choice.<n>We explore how different policies regarding the handling of user profiles interact with fairness outcomes for consumers and providers.
  arXiv  Detail & Related papers  (2025-08-28T07:38:59Z)
• Online Clustering of Dueling Bandits [59.09590979404303]
  We introduce the first "clustering of dueling bandit algorithms" to enable collaborative decision-making based on preference feedback.<n>We propose two novel algorithms: (1) Clustering of Linear Dueling Bandits (COLDB) which models the user reward functions as linear functions of the context vectors, and (2) Clustering of Neural Dueling Bandits (CONDB) which uses a neural network to model complex, non-linear user reward functions.
  arXiv  Detail & Related papers  (2025-02-04T07:55:41Z)
• When Online Algorithms Influence the Environment: A Dynamical Systems Analysis of the Unintended Consequences [5.4209739979186295]
  We analyze the effect that online algorithms have on the environment that they are learning. We show that when the recommendation algorithm is able to learn the population preferences in the presence of this mismatch, the algorithm induces similarity in the preferences of the user population.
  arXiv  Detail & Related papers  (2024-11-21T06:47:53Z)
• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis [69.37718774071793]
  This paper introduces novel information-theoretic measures for understanding recommender systems. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics.
  arXiv  Detail & Related papers  (2024-10-03T13:02:07Z)
• Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences [7.552217586057245]
  We propose a simulation framework that mimics user-recommender system interactions in a long-term scenario. We introduce two novel metrics for quantifying the algorithm's impact on user preferences, specifically in terms of drift over time.
  arXiv  Detail & Related papers  (2024-09-24T21:54:22Z)
• Graph Neural Bandits [49.85090929163639]
  We propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs) To refine the recommendation strategy, we utilize separate GNN-based models on estimated user graphs for exploitation and adaptive exploration.
  arXiv  Detail & Related papers  (2023-08-21T15:57:57Z)
• A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems [0.0]
  The bias amplification issue needs to be investigated while using these algorithms. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms.
  arXiv  Detail & Related papers  (2023-01-18T16:29:05Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.