# Multi-Gradient Descent for Multi-Objective Recommender Systems

arXiv: https://arxiv.org/abs/2001.00846 · HTML: https://mlsanity.com/paper/2001.00846/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 20 of 20)

- [Multi-Objective Meta Learning](https://mlsanity.com/paper/2102.07121/) (2021)
- [Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models](https://mlsanity.com/paper/2402.18059/) (2024)
- [Pareto Set Learning for Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2501.06773/) (2025)
- [Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond](https://mlsanity.com/paper/2501.10945/) (2025)
- [Improving Pareto Front Learning via Multi-Sample Hypernetworks](https://mlsanity.com/paper/2212.01130/) (2022)
- [Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2110.15097/) (2021)
- [PMGDA: A Preference-based Multiple Gradient Descent Algorithm](https://mlsanity.com/paper/2402.09492/) (2024)
- [Panoptic Perception for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.15388/) (2024)
- [Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks](https://mlsanity.com/paper/2412.17312/) (2024)
- [Self-Evolutionary Optimization for Pareto Front Learning](https://mlsanity.com/paper/2110.03461/) (2021)
- [Multi-task problems are not multi-objective](https://mlsanity.com/paper/2110.07301/) (2021)
- [Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems](https://mlsanity.com/paper/2407.16828/) (2024)
- [Federated Communication-Efficient Multi-Objective Optimization](https://mlsanity.com/paper/2410.16398/) (2024)
- [Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints](https://mlsanity.com/paper/2110.15442/) (2021)
- [Federated Semi-Supervised Domain Adaptation via Knowledge Transfer](https://mlsanity.com/paper/2207.10727/) (2022)
- [A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks](https://mlsanity.com/paper/2506.06715/) (2025)
- [PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic](https://mlsanity.com/paper/2108.09076/) (2021)
- [Controllable Pareto Trade-off between Fairness and Accuracy](https://mlsanity.com/paper/2509.13651/) (2025)
- [APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness](https://mlsanity.com/paper/2509.13908/) (2025)
- [Stealthy Poisoning Attacks Bypass Defenses in Regression Settings](https://mlsanity.com/paper/2601.22308/) (2026)

## Abstract (arXiv)

Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender system has. In addition there may be multiple stakeholders - sellers, buyers, shareholders - in addition to legal and ethical constraints. Simultaneously optimizing for a multitude of objectives, correlated and not correlated, having the same scale or not, has proven difficult so far. We introduce a stochastic multi-gradient descent approach to recommender systems (MGDRec) to solve this problem. We show that this exceeds state-of-the-art methods in traditional objective mixtures, like revenue and recall. Not only that, but through gradient normalization we can combine fundamentally different objectives, having diverse scales, into a single coherent framework. We show that uncorrelated objectives, like the proportion of quality products, can be improved alongside accuracy. Through the use of stochasticity, we avoid the pitfalls of calculating full gradients and provide a clear setting for its applicability.