# Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems

arXiv: https://arxiv.org/abs/2407.16828 · HTML: https://mlsanity.com/paper/2407.16828/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Pareto Multi-Task Learning](https://mlsanity.com/paper/1912.12854/) (2019)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Learning the Pareto Front with Hypernetworks](https://mlsanity.com/paper/2010.04104/) (2020)
- [Scalable Pareto Front Approximation for Deep Multi-Objective Learning](https://mlsanity.com/paper/2103.13392/) (2021)
- [On the Effectiveness of Sampled Softmax Loss for Item Recommendation](https://mlsanity.com/paper/2201.02327/) (2022)
- [Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning](https://mlsanity.com/paper/2201.00140/) (2022)
- [Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation](https://mlsanity.com/paper/2105.02951/) (2021)
- [Multi-Gradient Descent for Multi-Objective Recommender Systems](https://mlsanity.com/paper/2001.00846/) (2019)
- [Improving Pareto Front Learning via Multi-Sample Hypernetworks](https://mlsanity.com/paper/2212.01130/) (2022)
- [A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications](https://mlsanity.com/paper/2302.12487/) (2023)
- [The Hypervolume Indicator: Problems and Algorithms](https://mlsanity.com/paper/2005.00515/) (2020)
- [Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions](https://mlsanity.com/paper/2307.14906/) (2023)
- [Pareto-based Multi-Objective Recommender System with Forgetting Curve](https://mlsanity.com/paper/2312.16868/) (2023)
- [STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation](https://mlsanity.com/paper/2306.12232/) (2023)

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

- [Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond](https://mlsanity.com/paper/2501.10945/) (2025)
- [MultiScale Contextual Bandits for Long Term Objectives](https://mlsanity.com/paper/2503.17674/) (2025)
- [Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems](https://mlsanity.com/paper/2507.09566/) (2025)

## Abstract (arXiv)

This work introduces MultiTRON, an approach that adapts Pareto front approximation techniques to multi-objective session-based recommender systems using a transformer neural network. Our approach optimizes trade-offs between key metrics such as click-through and conversion rates by training on sampled preference vectors. A significant advantage is that after training, a single model can access the entire Pareto front, allowing it to be tailored to meet the specific requirements of different stakeholders by adjusting an additional input vector that weights the objectives. We validate the model's performance through extensive offline and online evaluation. For broader application and research, the source code is made available at https://github.com/otto-de/MultiTRON. The results confirm the model's ability to manage multiple recommendation objectives effectively, offering a flexible tool for diverse business needs.