# Active Learning in Recommendation Systems with Multi-level User Preferences

arXiv: https://arxiv.org/abs/1811.12591 · HTML: https://mlsanity.com/paper/1811.12591/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems](https://mlsanity.com/paper/2401.09034/) (2024)
- [ALScope: A Unified Toolkit for Deep Active Learning](https://mlsanity.com/paper/2508.04937/) (2025)
- [Explainable Active Learning for Preference Elicitation](https://mlsanity.com/paper/2309.00356/) (2023)

## Abstract (arXiv)

While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences. In such settings, considering queries at different levels of granularity to optimize user information acquisition is crucial to efficiently providing a good user experience. In this work, we study the active learning problem with multi-level user preferences within the collective matrix factorization (CMF) framework. CMF jointly captures multi-level user preferences with respect to items and relations between items (e.g., book genre, cuisine type), generally resulting in improved predictions. Motivated by finite-sample analysis of the CMF model, we propose a theoretically optimal active learning strategy based on the Fisher information matrix and use this to derive a realizable approximation algorithm for practical recommendations. Experiments are conducted using both the Yelp dataset directly and an illustrative synthetic dataset in the three settings of personalized active learning, cold-start recommendations, and noisy data -- demonstrating strong improvements over several widely used active learning methods.