Group Match Prediction via Neural Networks

Abstract

We consider the group match prediction problem where the goal is to estimate the probability of one group of items preferred over another, based on partially observed group comparison data. Most prior algorithms, each tailored to a specific statistical model, suffer from inconsistent performances across different scenarios. Motivated by a key common structure of the state-of-the-art, which we call the reward-penalty structure, we develop a unified framework that achieves consistently high performances across a wide range of scenarios. Our distinction lies in introducing neural networks embedded with the reward-penalty structure. Extensive experiments on synthetic and real-world datasets show that our framework consistently leads to the best performance, while the state-of-the-art perform inconsistently.