Soft Retargeting Network for Click Through Rate Prediction

TL;DR

This paper introduces a soft retargeting network (SRN) that models user retargeting interest in CTR prediction by leveraging graph embeddings to measure similarity between target and historical items, improving prediction accuracy.

Contribution

The paper proposes a novel SRN model that captures user retargeting interest and its evolution, enhancing CTR prediction performance over existing models.

Findings

SRN outperforms state-of-the-art models on public datasets.

Model effectively captures user retargeting interest.

Experimental results show significant improvement in CTR prediction.

Abstract

The study of user interest models has received a great deal of attention in click through rate (CTR) prediction recently. These models aim at capturing user interest from different perspectives, including user interest evolution, session interest, multiple interests, etc. In this paper, we focus on a new type of user interest, i.e., user retargeting interest. User retargeting interest is defined as user's click interest on target items the same as or similar to historical click items. We propose a novel soft retargeting network (SRN) to model this specific interest. Specifically, we first calculate the similarity between target item and each historical item with the help of graph embedding. Then we learn to aggregate the similarity weights to measure the extent of user's click interest on target item. Furthermore, we model the evolution of user retargeting interest. Experimental results on public datasets and industrial dataset demonstrate that our model achieves significant improvements over state-of-the-art models.