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.

翻译：群组推荐系统（GRS）在基于群组偏好而非个体偏好从近乎无限的项目库中发现相关项目时至关重要，例如向一组人推荐电影、餐厅或旅游目的地。传统的群组推荐模型设计为黑盒模式，严格专注于提升推荐准确性，且通常要求用户自行解读推荐结果。近年来，推荐系统（RS）研究的重点已从仅仅提升推荐准确性转向附加值功能，如置信度与解释性。本文提出一种共形预测框架，结合群组推荐系统为系统生成的普通推荐提供置信度衡量指标。针对群组推荐场景，我们提出了多种在共形框架效率中起关键作用的非一致性度量方法，并证明所定义的非一致性满足可交换性。实验结果表明，该方法在多个基准数据集上具有有效性。此外，所提方法还满足有效性与效率属性。