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

TL;DR

This paper introduces a novel Bayesian optimization framework with orthogonal meta-learning to improve uncertain multi-objective recommender systems, enabling personalized, ethical, and adaptive recommendations that balance accuracy, diversity, and fairness.

Contribution

It proposes an orthogonal meta-learning approach to enhance Bayesian optimization for uncertain multi-objective recommender systems, addressing personalization and objective conflicts.

Findings

Effective in optimizing multiple uncertain objectives for individual users

Improves recommendation diversity and fairness while maintaining accuracy

Enhances efficiency of Bayesian optimization through shared knowledge

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.