# MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

arXiv: https://arxiv.org/abs/1908.00413 · HTML: https://mlsanity.com/paper/1908.00413/

## Key facts

- Cited by 119 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 30 of 119)

- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023)
- [Personalized Transfer of User Preferences for Cross-domain Recommendation](https://mlsanity.com/paper/2110.11154/) (2021)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents](https://mlsanity.com/paper/2308.09904/) (2023)
- [Gradient-based Bi-level Optimization for Deep Learning: A Survey](https://mlsanity.com/paper/2207.11719/) (2022)
- [Cross-Market Product Recommendation](https://mlsanity.com/paper/2109.05929/) (2021)
- [A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps](https://mlsanity.com/paper/2106.03819/) (2021)
- [Comprehensive Fair Meta-learned Recommender System](https://mlsanity.com/paper/2206.04789/) (2022)
- [Language Representations Can be What Recommenders Need: Findings and Potentials](https://mlsanity.com/paper/2407.05441/) (2024)
- [Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation](https://mlsanity.com/paper/2405.00981/) (2024)
- [ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization](https://mlsanity.com/paper/2206.08181/) (2022)
- [Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations](https://mlsanity.com/paper/2306.17256/) (2023)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](https://mlsanity.com/paper/2105.08909/) (2021)
- [Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework](https://mlsanity.com/paper/2201.10980/) (2022)
- [Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://mlsanity.com/paper/2306.14462/) (2023)
- [Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation](https://mlsanity.com/paper/2311.18213/) (2023)
- [Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders](https://mlsanity.com/paper/2108.01053/) (2021)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
- [Node-Time Conditional Prompt Learning In Dynamic Graphs](https://mlsanity.com/paper/2405.13937/) (2024)
- [Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems](https://mlsanity.com/paper/2111.13463/) (2021)
- [Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation](https://mlsanity.com/paper/2205.06058/) (2022)
- [Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer](https://mlsanity.com/paper/2201.06095/) (2022)
- [MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation](https://mlsanity.com/paper/2202.03851/) (2022)
- [GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://mlsanity.com/paper/2407.12338/) (2024)
- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [Continuous-Time User Preference Modelling for Temporal Sets Prediction](https://mlsanity.com/paper/2204.05490/) (2022)

## Abstract (arXiv)

This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based on the items selected by the user. Previous recommendation studies have two limitations: (1) the users who consumed a few items have poor recommendations and (2) inadequate evidence candidates are used to identify user preferences. We propose a meta-learning-based recommender system called MeLU to overcome these two limitations. From meta-learning, which can rapidly adopt new task with a few examples, MeLU can estimate new user's preferences with a few consumed items. In addition, we provide an evidence candidate selection strategy that determines distinguishing items for customized preference estimation. We validate MeLU with two benchmark datasets, and the proposed model reduces at least 5.92% mean absolute error than two comparative models on the datasets. We also conduct a user study experiment to verify the evidence selection strategy.