UNISON: A Unified Framework for Learning from Scored Bags
UNISON creates a unified framework for learning from scored bags by treating multiple instance learning and recommendation systems as instances of learning from scored item-bag pairs. It decomposes the process into three stages: learning item representations, modeling bag-specific preferences via personalized parameters, and inferring bag characteristics. This approach achieves state-of-the-art performance across diverse domains with significant computational efficiency gains.