PARWiS: Winner determination under shoestring budgets using active pairwise comparisons

2026-03-01

AI Summary

This work addresses the problem of accurately identifying the best item from a candidate set under an extremely limited comparison budget. To this end, the authors propose PARWiS, an active winner identification algorithm that combines spectral ranking with a destructive pairwise selection strategy to efficiently utilize scarce comparisons. They further extend PARWiS into two variants: Contextual PARWiS, which incorporates contextual features, and RL PARWiS, which leverages reinforcement learning. Experiments on the Jester and MovieLens datasets demonstrate that the proposed methods consistently outperform baseline approaches such as Double Thompson Sampling in terms of recovery rate, winner rank, and cumulative regret—particularly in scenarios where the gap between the best and second-best items (Δ₁,₂) is large.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Determining a winner among a set of items using active pairwise comparisons under a limited budget is a challenging problem in preference-based learning. The goal of this study is to implement and evaluate the PARWiS algorithm, which shows spectral ranking and disruptive pair selection to identify the best item under shoestring budgets. This work have extended the PARWiS with a contextual variant (Contextual PARWiS) and a reinforcement learning-based variant (RL PARWiS), comparing them against baselines, including Double Thompson Sampling and a random selection strategy. This evaluation spans synthetic and real-world datasets (Jester and MovieLens), using budgets of 40, 60, and 80 comparisons for 20 items. The performance is measured through recovery fraction, true rank of reported winner, reported rank of true winner, and cumulative regret, alongside the separation metric \(Δ_{1,2}\). Results show that PARWiS and RL PARWiS outperform baselines across all datasets, particularly in the Jester dataset with a higher \(Δ_{1,2}\), while performance gaps narrow in the more challenging MovieLens dataset with a smaller \(Δ_{1,2}\). Contextual PARWiS shows comparable performance to PARWiS, indicating that contextual features may require further tuning to provide significant benefits.

Problem

• winner determination
• active pairwise comparisons
• shoestring budgets
• preference-based learning
• best arm identification

Innovation

• active pairwise comparisons
• shoestring budgets
• spectral ranking
• disruptive pair selection
• reinforcement learning