# Multi-Task Deep Recommender Systems: A Survey

arXiv: https://arxiv.org/abs/2302.03525 · HTML: https://mlsanity.com/paper/2302.03525/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2023.

## Built on by (most cited first, 30 of 30)

- [When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications](https://mlsanity.com/paper/2310.18339/) (2023)
- [Representation Surgery for Multi-Task Model Merging](https://mlsanity.com/paper/2402.02705/) (2024)
- [M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework](https://mlsanity.com/paper/2404.18465/) (2024)
- [Dual-Balancing for Multi-Task Learning](https://mlsanity.com/paper/2308.12029/) (2023)
- [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)
- [Training-free LLM Merging for Multi-task Learning](https://mlsanity.com/paper/2506.12379/) (2025)
- [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)
- [GPRec: Bi-level User Modeling for Deep Recommenders](https://mlsanity.com/paper/2410.20730/) (2024)
- [The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion](https://mlsanity.com/paper/2505.05605/) (2025)
- [MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning](https://mlsanity.com/paper/2504.00460/) (2025)
- [Macro Graph of Experts for Billion-Scale Multi-Task Recommendation](https://mlsanity.com/paper/2506.10520/) (2025)
- [PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching](https://mlsanity.com/paper/2506.18382/) (2025)
- [Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs](https://mlsanity.com/paper/2509.02017/) (2025)
- [Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering](https://mlsanity.com/paper/2311.06777/) (2023)
- [BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation](https://mlsanity.com/paper/2402.12733/) (2024)
- [Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning](https://mlsanity.com/paper/2407.00082/) (2024)
- [No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods](https://mlsanity.com/paper/2412.12092/) (2024)
- [A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs](https://mlsanity.com/paper/2506.14235/) (2025)
- [GRADE: Personalized Multi-Task Fusion via Group-relative Reinforcement Learning with Adaptive Dirichlet Exploration](https://mlsanity.com/paper/2510.07919/) (2025)
- [Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models](https://mlsanity.com/paper/2510.17705/) (2025)
- [Efficient Multi-Task Modeling through Automated Fusion of Trained Models](https://mlsanity.com/paper/2504.09812/) (2025)
- [Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data](https://mlsanity.com/paper/2508.09636/) (2025)
- [Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies](https://mlsanity.com/paper/2601.02754/) (2026)
- [MDL: A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization](https://mlsanity.com/paper/2602.07520/) (2026)
- [Action-Aware Generative Sequence Modeling for Short Video Recommendation](https://mlsanity.com/paper/2604.25834/) (2026)
- [DRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepage](https://mlsanity.com/paper/2610.15696/) (2026)

## Abstract (arXiv)

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic in recommendation due to the demand for multi-task prediction considering performance and efficiency. Although MTL has been well studied and developed, there is still a lack of systematic review in the recommendation community. To fill the gap, we provide a comprehensive review of existing multi-task deep recommender systems (MTDRS) in this survey. To be specific, the problem definition of MTDRS is first given, and it is compared with other related areas. Next, the development of MTDRS is depicted and the taxonomy is introduced from the task relation and methodology aspects. Specifically, the task relation is categorized into parallel, cascaded, and auxiliary with main, while the methodology is grouped into parameter sharing, optimization, and training mechanism. The survey concludes by summarizing the application and public datasets of MTDRS and highlighting the challenges and future directions of the field.