Recommendation Fairness: From Static to Dynamic

• URL: http://arxiv.org/abs/2109.03150v1
• Date: Sun, 5 Sep 2021 21:38:05 GMT
• Title: Recommendation Fairness: From Static to Dynamic
• Authors: Dell Zhang and Jun Wang
• Abstract summary: We discuss how fairness could be baked into reinforcement learning techniques for recommendation. We argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization.
• Score: 12.080824433982993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulation-based optimization, in the general framework of stochastic games.

Related papers

• Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method [60.364834418531366]
  We propose five new evaluation metrics that comprehensively and accurately assess the performance of RRS. We formulate the RRS from a causal perspective, formulating recommendations as bilateral interventions. We introduce a reranking strategy to maximize matching outcomes, as measured by the proposed metrics.
  arXiv  Detail & Related papers  (2024-08-19T07:21:02Z)
• Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation [0.0]
  We are the first to apply the Fisher-Merging method to Sequential Recommendation, addressing and resolving practical challenges associated with it. We demonstrate the effectiveness of our proposed methods, highlighting their potential to advance the state-of-the-art in sequential learning and recommendation systems.
  arXiv  Detail & Related papers  (2023-07-05T05:58:56Z)
• A Survey on Fairness-aware Recommender Systems [59.23208133653637]
  We present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, we delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications.
  arXiv  Detail & Related papers  (2023-06-01T07:08:22Z)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• CausPref: Causal Preference Learning for Out-of-Distribution Recommendation [36.22965012642248]
  The current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios. We propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref. Our approach surpasses the benchmark models significantly under types of out-of-distribution settings.
  arXiv  Detail & Related papers  (2022-02-08T16:42:03Z)
• D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning [0.3058685580689604]
  This paper proposes a deep reinforcement learning based recommendation system by utilizing Actor-Critic architecture. The proposed model is able to generate a diverse while relevance recommendation list based on the user's preferences.
  arXiv  Detail & Related papers  (2021-10-28T13:11:29Z)
• Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation [7.98348797868119]
  Agent-Based Modeling and Simulation (ABM) techniques can be used to study such important longitudinal dynamics of recommender systems. We provide an overview of the ABM principles, outline a simulation framework for recommender systems based on the literature, and discuss various practical research questions that can be addressed with such an ABM-based simulation framework.
  arXiv  Detail & Related papers  (2021-08-25T06:28:19Z)
• Fast Multi-Step Critiquing for VAE-based Recommender Systems [27.207067974031805]
  We present M&Ms-VAE, a novel variational autoencoder for recommendation and explanation. We train the model under a weak supervision scheme to simulate both fully and partially observed variables. We then leverage the generalization ability of a trained M&Ms-VAE model to embed the user preference and the critique separately.
  arXiv  Detail & Related papers  (2021-05-03T12:26:09Z)
• Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect [89.91745908462417]
  We show the benefits of pre-training to recommender systems through experiments. We discuss several promising directions for future research for recommender systems with pre-training.
  arXiv  Detail & Related papers  (2020-09-19T13:06:27Z)
• Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
  We propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
  arXiv  Detail & Related papers  (2020-06-10T11:18:57Z)
• 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.