# MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

arXiv: https://arxiv.org/abs/2007.03183 · HTML: https://mlsanity.com/paper/2007.03183/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 30 of 39)

- [Zero-Shot Recommender Systems](https://mlsanity.com/paper/2105.08318/) (2021)
- [Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2304.08183/) (2023)
- [Comprehensive Fair Meta-learned Recommender System](https://mlsanity.com/paper/2206.04789/) (2022)
- [Meta-Inductive Node Classification across Graphs](https://mlsanity.com/paper/2105.06725/) (2021)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://mlsanity.com/paper/2306.14462/) (2023)
- [Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer](https://mlsanity.com/paper/2201.06095/) (2022)
- [Deep trip generation with graph neural networks for bike sharing system expansion](https://mlsanity.com/paper/2303.11977/) (2023)
- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems](https://mlsanity.com/paper/2309.15646/) (2023)
- [KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation](https://mlsanity.com/paper/2508.05633/) (2025)
- [Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations](https://mlsanity.com/paper/2204.00327/) (2022)
- [Sequential LLM Framework for Fashion Recommendation](https://mlsanity.com/paper/2410.11327/) (2024)
- [CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation](https://mlsanity.com/paper/2108.10511/) (2021)
- [Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling](https://mlsanity.com/paper/2208.09130/) (2022)
- [ColdNAS: Search to Modulate for User Cold-Start Recommendation](https://mlsanity.com/paper/2306.03387/) (2023)
- [Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach](https://mlsanity.com/paper/2307.01204/) (2023)
- [IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation](https://mlsanity.com/paper/2208.04600/) (2022)
- [Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation](https://mlsanity.com/paper/2302.14640/) (2023)
- [Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce](https://mlsanity.com/paper/2306.05011/) (2023)
- [Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems](https://mlsanity.com/paper/2309.01188/) (2023)
- [Knowledge-Driven New Drug Recommendation](https://mlsanity.com/paper/2210.05572/) (2022)
- [GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning](https://mlsanity.com/paper/2304.12537/) (2023)
- [Proxy-based Item Representation for Attribute and Context-aware Recommendation](https://mlsanity.com/paper/2312.06145/) (2023)
- [All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction](https://mlsanity.com/paper/2403.17740/) (2024)
- [Learning Social Graph for Inactive User Recommendation](https://mlsanity.com/paper/2405.05288/) (2024)
- [LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks](https://mlsanity.com/paper/2604.12096/) (2026)
- [RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction](https://mlsanity.com/paper/2210.16080/) (2022)
- [Meta-Shop: Improving Item Advertisement For Small Businesses](https://mlsanity.com/paper/2212.01414/) (2022)
- [Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning](https://mlsanity.com/paper/2212.03423/) (2022)

## Abstract (arXiv)

A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handle situations with new users or new items. Recently, some works introduce the meta-optimization idea into the recommendation scenarios, i.e. predicting the user preference by only a few of past interacted items. The core idea is learning a global sharing initialization parameter for all users and then learning the local parameters for each user separately. However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users. In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories. Specifically, the feature-specific memories are used to guide the model with personalized parameter initialization, while the task-specific memories are used to guide the model fast predicting the user preference. And we adopt a meta-optimization approach for optimizing the proposed method. We test the model on two widely used recommendation datasets and consider four cold-start situations. The experimental results show the effectiveness of the proposed methods.