Conformal Group Recommender System

TL;DR

This paper introduces a conformal prediction framework for group recommender systems that provides confidence measures with recommendations, enhancing transparency and reliability beyond traditional accuracy-focused models.

Contribution

It proposes a novel conformal prediction approach with nonconformity measures tailored for group recommendations, ensuring validity and efficiency.

Findings

Effective confidence measures demonstrated on benchmark datasets

Framework satisfies validity and efficiency properties

Improves transparency in group recommendations

Abstract

Group recommender systems (GRS) are critical in discovering relevant items from a near-infinite inventory based on group preferences rather than individual preferences, like recommending a movie, restaurant, or tourist destination to a group of individuals. The traditional models of group recommendation are designed to act like a black box with a strict focus on improving recommendation accuracy, and most often, they place the onus on the users to interpret recommendations. In recent years, the focus of Recommender Systems (RS) research has shifted away from merely improving recommendation accuracy towards value additions such as confidence and explanation. In this work, we propose a conformal prediction framework that provides a measure of confidence with prediction in conjunction with a group recommender system to augment the system-generated plain recommendations. In the context of group recommender systems, we propose various nonconformity measures that play a vital role in the efficiency of the conformal framework. We also show that defined nonconformity satisfies the exchangeability property. Experimental results demonstrate the effectiveness of the proposed approach over several benchmark datasets. Furthermore, our proposed approach also satisfies validity and efficiency properties.