# Solving the Cold Start Problem on One's Own as an End User via Preference Transfer

arXiv: https://arxiv.org/abs/2502.12398 · HTML: https://mlsanity.com/paper/2502.12398/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions](https://mlsanity.com/paper/1502.06800/) (2015)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Automated Scalable Bayesian Inference via Hilbert Coresets](https://mlsanity.com/paper/1710.05053/) (2017)
- [Kernel Thinning](https://mlsanity.com/paper/2105.05842/) (2021)
- [Discrepancy, Coresets, and Sketches in Machine Learning](https://mlsanity.com/paper/1906.04845/) (2019)
- [Positively Weighted Kernel Quadrature via Subsampling](https://mlsanity.com/paper/2107.09597/) (2021)
- [Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees](https://mlsanity.com/paper/1506.02681/) (2015)
- [Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?](https://mlsanity.com/paper/2105.12353/) (2021)
- [Towards Principled User-side Recommender Systems](https://mlsanity.com/paper/2208.09864/) (2022)
- [Retrieving Black-box Optimal Images from External Databases](https://mlsanity.com/paper/2112.14921/) (2021)
- [CLEAR: A Fully User-side Image Search System](https://mlsanity.com/paper/2206.08521/) (2022)
- [Making Translators Privacy-aware on the User's Side](https://mlsanity.com/paper/2312.04068/) (2023)
- [User-Side Realization](https://mlsanity.com/paper/2403.15757/) (2024)
- [Overhead-free User-side Recommender Systems](https://mlsanity.com/paper/2411.07589/) (2024)

## Built on by (most cited first, 1 of 1)

- [AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side](https://mlsanity.com/paper/2609.31166/) (2026)

## Abstract (arXiv)

We propose a new approach that enables end users to directly solve the cold start problem by themselves. The cold start problem is a common issue in recommender systems, and many methods have been proposed to address the problem on the service provider's side. However, when the service provider does not take action, users are left with poor recommendations and no means to improve their experience. We propose an algorithm, Pretender, that allows end users to proactively solve the cold start problem on their own. Pretender does not require any special support from the service provider and can be deployed independently by users. We formulate the problem as minimizing the distance between the source and target distributions and optimize item selection from the target service accordingly. Furthermore, we establish theoretical guarantees for Pretender based on a discrete quadrature problem. We conduct experiments on real-world datasets to demonstrate the effectiveness of Pretender.