A knowledge distillation-based deep interaction compressed network for CTR prediction

Abstract

Click-through rate (CTR) prediction is an essential task in online advertising recommender systems. As a hot research frontier driven by academic and industrial needs, CTR prediction has observed remarkable progress in recent years. How to effectively learn sophisticated feature interactions hidden behind user behaviors is critical in maximizing CTR. Targeted at the above issue, this paper first constructs a deep interaction compressed network model (abbreviated as DICN) that aims to generate both low- and high-order feature interactions with different weights in both explicit and implicit fashions. Then, a KD-DICN model is created using a knowledge distillation framework to reduce the complexity of DICN. Finally, comprehensive experiments are conducted on two public datasets to demonstrate the effectiveness and efficiency of our models, which outperform the existing state-of-the-art methods. Therefore, the research presented in this paper can not only enrich and develop the theories related to CTR prediction, but it can also assist some advertising industries in better defining their service audience and recommending more popular contents to consumers more accurately.