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

arXiv: https://arxiv.org/abs/2502.13180 · HTML: https://mlsanity.com/paper/2502.13180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 28 of 28)

- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [Fairness in Recommendation Ranking through Pairwise Comparisons](https://mlsanity.com/paper/1903.00780/) (2019)
- [Compositional Fairness Constraints for Graph Embeddings](https://mlsanity.com/paper/1905.10674/) (2019)
- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://mlsanity.com/paper/2105.06067/) (2021)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Towards Long-term Fairness in Recommendation](https://mlsanity.com/paper/2101.03584/) (2021)
- [Learning Fair Representations for Recommendation: A Graph-based Perspective](https://mlsanity.com/paper/2102.09140/) (2021)
- [DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://mlsanity.com/paper/2211.10486/) (2022)
- [TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers](https://mlsanity.com/paper/2104.09024/) (2021)
- [Large Language Models for Intent-Driven Session Recommendations](https://mlsanity.com/paper/2312.07552/) (2023)
- [DGCN: Diversified Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2108.06952/) (2021)
- [Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation](https://mlsanity.com/paper/2105.02951/) (2021)
- [Research Commentary on Recommendations with Side Information: A Survey and Research Directions](https://mlsanity.com/paper/1909.12807/) (2019)
- [User-controllable Recommendation Against Filter Bubbles](https://mlsanity.com/paper/2204.13844/) (2022)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Improving End-to-End Sequential Recommendations with Intent-aware Diversification](https://mlsanity.com/paper/1908.10171/) (2019)
- [Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation](https://mlsanity.com/paper/2206.05020/) (2022)
- [Recommending Accurate and Diverse Items Using Bilateral Branch Network](https://mlsanity.com/paper/2101.00781/) (2021)
- [Future-Aware Diverse Trends Framework for Recommendation](https://mlsanity.com/paper/2011.00422/) (2020)
- [Determinantal Point Process Likelihoods for Sequential Recommendation](https://mlsanity.com/paper/2204.11562/) (2022)
- [Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System](https://mlsanity.com/paper/2008.13526/) (2020)
- [Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities](https://mlsanity.com/paper/2308.09309/) (2023)
- [Deep Pareto Reinforcement Learning for Multi-Objective Recommender Systems](https://mlsanity.com/paper/2407.03580/) (2024)
- [A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation](https://mlsanity.com/paper/2212.07030/) (2022)

## Built on by (most cited first, 3 of 3)

- [GRADE: Personalized Multi-Task Fusion via Group-relative Reinforcement Learning with Adaptive Dirichlet Exploration](https://mlsanity.com/paper/2510.07919/) (2025)
- [A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting](https://mlsanity.com/paper/2602.01445/) (2026)
- [First, do no harm: Breaking suicidogenic echo chambers in media recommendation](https://mlsanity.com/paper/2605.25258/) (2026)

## Abstract (arXiv)

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.