1 research outputs found
Detecting Beneficial Feature Interactions for Recommender Systems
Feature interactions are essential for achieving high accuracy in recommender
systems. Many studies take into account the interaction between every pair of
features. However, this is suboptimal because some feature interactions may not
be that relevant to the recommendation result, and taking them into account may
introduce noise and decrease recommendation accuracy. To make the best out of
feature interactions, we propose a graph neural network approach to effectively
model them, together with a novel technique to automatically detect those
feature interactions that are beneficial in terms of recommendation accuracy.
The automatic feature interaction detection is achieved via edge prediction
with an L0 activation regularization. Our proposed model is proved to be
effective through the information bottleneck principle and statistical
interaction theory. Experimental results show that our model (i) outperforms
existing baselines in terms of accuracy, and (ii) automatically identifies
beneficial feature interactions.Comment: 14 pages, 7 figures, 5 table