Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods

Meng Yan, Cai Xu, Xujing Wang, Ziyu Guan, Wei Zhao, Yuhang Zhou

Abstract:

Recommender systems based on graph neural networks perform
well in tasks such as rating and ranking. However, in real-world
recommendation scenarios, noise such as user misuse and malicious advertisement gradually accumulates through the message
propagation mechanism. Even if existing studies mitigate their effects by reducing the noise propagation weights, the severe sparsity
of the recommender system still leads to the low-weighted noisy
neighbors being mistaken as meaningful information, and the prediction result obtained based on the polluted nodes is not entirely
trustworthy. Therefore, it is crucial to measure the confidence of the
prediction results in this highly noisy framework. Furthermore, our
evaluation of the existing representative GNN-based recommendation shows that it suffers from overconfidence. Based on the above
considerations, we propose a new method to quantify and calibrate
the prediction confidence of GNN-based recommendations (Conf-
GNNRec). Specifically, we propose a rating calibration method that
dynamically adjusts excessive ratings to mitigate overconfidence
based on user personalization. We also design a confidence loss
function to reduce the overconfidence of negative samples and
effectively improve recommendation performance. Experiments
on public datasets demonstrate the validity of Conf-GNNRec in
prediction confidence and recommendation performance.