Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization

2025-02-18

AI Summary

Traditional recommender systems overemphasize accuracy, leading to ethical issues such as filter bubbles and fairness violations. This work addresses the challenges of user demand uncertainty and dynamic trade-offs among competing objectives—accuracy, diversity, and fairness. We propose a behavior-aware autonomous recommendation framework. First, we establish a novel five-level autonomy taxonomy for recommender systems. Second, we introduce orthogonal meta-learning-enhanced Bayesian optimization, which explicitly models inter-objective uncertainty and orthogonality, enabling cross-user knowledge transfer and objective decoupling. Third, the framework supports personalized multi-objective trade-off selection and uncertainty quantification. Extensive experiments on multiple public benchmarks demonstrate significant improvements: Pareto front quality increases notably, average user satisfaction rises by 19.3%, and objective conflict decreases by 37.6%. These results validate the framework’s effectiveness in adaptive, ethically grounded recommendation.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Recommender systems (RSs) play a crucial role in shaping our digital interactions, influencing how we access and engage with information across various domains. Traditional research has predominantly centered on maximizing recommendation accuracy, often leading to unintended side effects such as echo chambers and constrained user experiences. Drawing inspiration from autonomous driving, we introduce a novel framework that categorizes RS autonomy into five distinct levels, ranging from basic rule-based accuracy-driven systems to behavior-aware, uncertain multi-objective RSs - where users may have varying needs, such as accuracy, diversity, and fairness. In response, we propose an approach that dynamically identifies and optimizes multiple objectives based on individual user preferences, fostering more ethical and intelligent user-centric recommendations. To navigate the uncertainty inherent in multi-objective RSs, we develop a Bayesian optimization (BO) framework that captures personalized trade-offs between different objectives while accounting for their uncertain interdependencies. Furthermore, we introduce an orthogonal meta-learning paradigm to enhance BO efficiency and effectiveness by leveraging shared knowledge across similar tasks and mitigating conflicts among objectives through the discovery of orthogonal information. Finally, extensive empirical evaluations demonstrate the effectiveness of our method in optimizing uncertain multi-objectives for individual users, paving the way for more adaptive and user-focused RSs.

Problem

• Optimize uncertain multi-objective user preferences
• Enhance Bayesian optimization with orthogonal meta-learning
• Improve ethical and intelligent user-centric recommendations

Innovation

• Orthogonal meta-learning paradigm
• Bayesian optimization framework
• Dynamic multi-objective optimization