Personalized Recommendations via Active Utility-based Pairwise Sampling

TL;DR

This paper introduces a flexible, utility-based active learning framework for personalized recommendations that learns preferences from pairwise comparisons, improving accuracy and efficiency over traditional rating-based methods.

Contribution

It proposes a novel utility-based active sampling strategy for preference elicitation within a model-agnostic framework, adaptable to various applications and utility functions.

Findings

Enhanced recommendation accuracy with fewer data points.

Versatility demonstrated through movie and university admission experiments.

Improved user-centric preference modeling.

Abstract

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.