# AutoLossGen: Automatic Loss Function Generation for Recommender Systems arXiv: https://arxiv.org/abs/2204.13160 ยท HTML: https://mlsanity.com/paper/2204.13160/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 60 references that are in the map. - First posted on arXiv 27 Apr 2022. ## Builds on (most cited first, 30 of 32) - [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015) - [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018) - [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017) - [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018) - [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018) - [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016) - [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017) - [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016) - [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016) - [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017) - [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022) - [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017) - [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016) - [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016) - [Neural Optimizer Search with Reinforcement Learning](https://mlsanity.com/paper/1709.07417/) (2017) - [Automated Machine Learning: From Principles to Practices](https://mlsanity.com/paper/1810.13306/) (2018) - [Path-Level Network Transformation for Efficient Architecture Search](https://mlsanity.com/paper/1806.02639/) (2018) - [Simple And Efficient Architecture Search for Convolutional Neural Networks](https://mlsanity.com/paper/1711.04528/) (2017) - [Neural Logic Reasoning](https://mlsanity.com/paper/2008.09514/) (2020) - [Neural Collaborative Reasoning](https://mlsanity.com/paper/2005.08129/) (2020) - [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021) - [AM-LFS: AutoML for Loss Function Search](https://mlsanity.com/paper/1905.07375/) (2019) - [Loss Function Search for Face Recognition](https://mlsanity.com/paper/2007.06542/) (2020) - [Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search](https://mlsanity.com/paper/2102.04700/) (2021) - [AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks](https://mlsanity.com/paper/2103.14026/) (2021) - [GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning](https://mlsanity.com/paper/1804.06964/) (2018) - [Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation](https://mlsanity.com/paper/2010.07930/) (2020) - [VEST: Automatic Feature Engineering for Forecasting](https://mlsanity.com/paper/2010.07137/) (2020) - [Graph Collaborative Reasoning](https://mlsanity.com/paper/2112.13705/) (2021) - [Causal Collaborative Filtering](https://mlsanity.com/paper/2102.01868/) (2021) ## Built on by (most cited first, 10 of 10) - [AutoFlow: Automated Workflow Generation for Large Language Model Agents](https://mlsanity.com/paper/2407.12821/) (2024) - [Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback](https://mlsanity.com/paper/2602.12612/) (2026) - [Toward a Better Understanding of Loss Functions for Collaborative Filtering](https://mlsanity.com/paper/2308.06091/) (2023) - [Automated Contrastive Learning Strategy Search for Time Series](https://mlsanity.com/paper/2403.12641/) (2024) - [Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)](https://mlsanity.com/paper/2208.11083/) (2022) - [Graph-based Alignment and Uniformity for Recommendation](https://mlsanity.com/paper/2308.09292/) (2023) - [PAP-REC: Personalized Automatic Prompt for Recommendation Language Model](https://mlsanity.com/paper/2402.00284/) (2024) - [Advancing Loss Functions in Recommender Systems: A Comparative Study with a R'enyi Divergence-Based Solution](https://mlsanity.com/paper/2506.15120/) (2025) - [Multi-Margin Cosine Loss: Proposal and Application in Recommender Systems](https://mlsanity.com/paper/2405.04614/) (2024) - [KLAN: Kuaishou Landing-page Adaptive Navigator](https://mlsanity.com/paper/2507.23459/) (2025) ## Abstract (arXiv) In recommendation systems, the choice of loss function is critical since a good loss may significantly improve the model performance. However, manually designing a good loss is a big challenge due to the complexity of the problem. A large fraction of previous work focuses on handcrafted loss functions, which needs significant expertise and human effort. In this paper, inspired by the recent development of automated machine learning, we propose an automatic loss function generation framework, AutoLossGen, which is able to generate loss functions directly constructed from basic mathematical operators without prior knowledge on loss structure. More specifically, we develop a controller model driven by reinforcement learning to generate loss functions, and develop iterative and alternating optimization schedule to update the parameters of both the controller model and the recommender model. One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search. To solve the problem, we further develop a reward filtering mechanism for efficient and effective loss generation. Experimental results show that our framework manages to create tailored loss functions for different recommendation models and datasets, and the generated loss gives better recommendation performance than commonly used baseline losses. Besides, most of the generated losses are transferable, i.e., the loss generated based on one model and dataset also works well for another model or dataset. Source code of the work is available at https://github.com/rutgerswiselab/AutoLossGen.