# Personalized Recommendations via Active Utility-based Pairwise Sampling

arXiv: https://arxiv.org/abs/2508.14911 · HTML: https://mlsanity.com/paper/2508.14911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Outer Product-based Neural Collaborative Filtering](https://mlsanity.com/paper/1808.03912/) (2018)
- [Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings](https://mlsanity.com/paper/1806.05085/) (2018)
- [Neural Collaborative Ranking](https://mlsanity.com/paper/1808.04957/) (2018)
- [Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model](https://mlsanity.com/paper/1904.03099/) (2019)

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

- [Efficient Personalization of Generative User Interfaces](https://mlsanity.com/paper/2604.09876/) (2026)

## Abstract (arXiv)

Recommender systems play a critical role in enhancing user experience by providing personalized suggestions based on user preferences. Traditional approaches often rely on explicit numerical ratings or assume access to fully ranked lists of items. However, ratings frequently fail to capture true preferences due to users' behavioral biases and subjective interpretations of rating scales, while eliciting full rankings is demanding and impractical. To overcome these limitations, we propose a generalized utility-based framework that learns preferences from simple and intuitive pairwise comparisons. Our approach is model-agnostic and designed to optimize for arbitrary, task-specific utility functions, allowing the system's objective to be explicitly aligned with the definition of a high-quality outcome in any given application. A central contribution of our work is a novel utility-based active sampling strategy for preference elicitation. This method selects queries that are expected to provide the greatest improvement to the utility of the final recommended outcome. We ground our preference model in the probabilistic Plackett-Luce framework for pairwise data. To demonstrate the versatility of our approach, we present two distinct experiments: first, an implementation using matrix factorization for a classic movie recommendation task, and second, an implementation using a neural network for a complex candidate selection scenario in university admissions. Experimental results demonstrate that our framework provides a more accurate, data-efficient, and user-centric paradigm for personalized ranking.