MetaEDL: Meta Evidential Learning For Uncertainty-Aware Cold-Start Recommendations

Abstract

Recommender systems have been widely used to predict users’ interests and filter information from a large number of candidate items. However, accurately capturing the interests of users having limited interactions with a system remains a long-lasting challenge. Furthermore, existing recommender systems primarily focus on predicting user preferences without quantifying the prediction uncertainty. Uncertainty can help to quantify the model confidence when making a recommendation where low model confidence could serve as a more accurate indicator of a user’s cold-start level than simply using the number of interactions. We present a novel recommendation model that seamlessly integrates a meta-learning module with an evidential learning approach. The former module generalizes meta knowledge to tackle cold-start recommendations by exploiting fast adaptation. The latter quantifies both aleatoric and epistemic uncertainty without performing expensive posterior inference. Evidential learning achieves this by placing evidential priors and treating the output of the meta-learning module as evidence-based pseudo counts and learns a function to directly predict the evidence of a target interaction. Experiments on four benchmark datasets justify that our proposed model captures the uncertainty of users and demonstrates its superior performance over the state-of-the-art recommendation models.