# Handling Cold-Start Collaborative Filtering with Reinforcement Learning

arXiv: https://arxiv.org/abs/1806.06192 · HTML: https://mlsanity.com/paper/1806.06192/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Contextual Bandits with Sparse Data in Web setting](https://mlsanity.com/paper/2105.02873/) (2021)
- [Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs](https://mlsanity.com/paper/2508.21259/) (2025)

## Abstract (arXiv)

A major challenge in recommender systems is handling new users, whom are also called textitcold-start users. In this paper, we propose a novel approach for learning an optimal series of questions with which to interview cold-start users for movie recommender systems. We propose learning interview questions using Deep Q Networks to create user profiles to make better recommendations to cold-start users. While our proposed system is trained using a movie recommender system, our Deep Q Network model should generalize across various types of recommender systems.