# Multi-Task Recommendations with Reinforcement Learning

arXiv: https://arxiv.org/abs/2302.03328 · HTML: https://mlsanity.com/paper/2302.03328/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 63 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Builds on (most cited first, 30 of 30)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018)
- [Deep Reinforcement Learning for Page-wise Recommendations](https://mlsanity.com/paper/1805.02343/) (2018)
- [Multi-task Sequence to Sequence Learning](https://mlsanity.com/paper/1511.06114/) (2015)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems](https://mlsanity.com/paper/1902.05570/) (2019)
- [KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos](https://mlsanity.com/paper/2208.08696/) (2022)
- [Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning](https://mlsanity.com/paper/1802.06501/) (2018)
- [Deep Multi-task Representation Learning: A Tensor Factorisation Approach](https://mlsanity.com/paper/1605.06391/) (2016)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [Deep Reinforcement Learning for List-wise Recommendations](https://mlsanity.com/paper/1801.00209/) (2017)
- [Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation](https://mlsanity.com/paper/1911.03845/) (2019)
- [Security and Privacy on Blockchain](https://mlsanity.com/paper/1903.07602/) (2019)
- [Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling](https://mlsanity.com/paper/1810.12027/) (2018)
- [Large-scale Interactive Recommendation with Tree-structured Policy Gradient](https://mlsanity.com/paper/1811.05869/) (2018)
- [Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1809.06260/) (2018)
- [Deep reinforcement learning for search, recommendation, and online advertising: a survey](https://mlsanity.com/paper/1812.07127/) (2018)
- [Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems](https://mlsanity.com/paper/2208.04560/) (2022)
- [Kalman Filtering Attention for User Behavior Modeling in CTR Prediction](https://mlsanity.com/paper/2010.00985/) (2020)
- [Rank and Rate: Multi-task Learning for Recommender Systems](https://mlsanity.com/paper/1807.11698/) (2018)
- [AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online](https://mlsanity.com/paper/2003.11941/) (2020)

## Built on by (most cited first, 26 of 26)

- [When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications](https://mlsanity.com/paper/2310.18339/) (2023)
- [Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning](https://mlsanity.com/paper/2505.14069/) (2025)
- [LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation](https://mlsanity.com/paper/2405.20646/) (2024)
- [M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework](https://mlsanity.com/paper/2404.18465/) (2024)
- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation](https://mlsanity.com/paper/2406.12529/) (2024)
- [Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2504.18383/) (2025)
- [SIGMA: Selective Gated Mamba for Sequential Recommendation](https://mlsanity.com/paper/2408.11451/) (2024)
- [Efficient and Robust Regularized Federated Recommendation](https://mlsanity.com/paper/2411.01540/) (2024)
- [Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models](https://mlsanity.com/paper/2412.04107/) (2024)
- [SampleLLM: Optimizing Tabular Data Synthesis in Recommendations](https://mlsanity.com/paper/2501.16125/) (2025)
- [Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model](https://mlsanity.com/paper/2403.17425/) (2024)
- [EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention](https://mlsanity.com/paper/2403.17729/) (2024)
- [GPRec: Bi-level User Modeling for Deep Recommenders](https://mlsanity.com/paper/2410.20730/) (2024)
- [Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems](https://mlsanity.com/paper/2506.23090/) (2025)
- [AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation](https://mlsanity.com/paper/2308.06965/) (2023)
- [Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation](https://mlsanity.com/paper/2310.16566/) (2023)
- [Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning](https://mlsanity.com/paper/2407.00082/) (2024)
- [Goal-Conditioned Supervised Learning for Multi-Objective Recommendation](https://mlsanity.com/paper/2412.08911/) (2024)
- [Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models](https://mlsanity.com/paper/2510.17705/) (2025)
- [Generative Archetype-Grounded Item Representations for Sequential Recommendation](https://mlsanity.com/paper/2606.11023/) (2026)
- [Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning](https://mlsanity.com/paper/2607.14192/) (2026)
- [Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation](https://mlsanity.com/paper/2407.18937/) (2024)
- [Hyperproperty-Constrained Secure Reinforcement Learning](https://mlsanity.com/paper/2508.00106/) (2025)
- [SEGB: Self-Evolved Generative Bidding with Local Autoregressive Diffusion](https://mlsanity.com/paper/2602.22226/) (2025)
- [Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding](https://mlsanity.com/paper/2607.28659/) (2026)

## Abstract (arXiv)

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL's compatibility and transferability across various MTL models.