A Deep Behavior Path Matching Network for Click-Through Rate Prediction

TL;DR

This paper introduces a deep neural network that matches user behavior paths to improve click-through rate prediction, addressing challenges like data sparsity and noise, and demonstrating superior performance on real-world datasets.

Contribution

The paper proposes a novel behavior path matching network utilizing contrastive learning and a two-level matching mechanism for CTR prediction.

Findings

Achieved 1.6% CTR improvement on Meituan platform.

Outperformed state-of-the-art CTR models on two datasets.

Effectively mitigated noise and sparsity in behavior data.

Abstract

User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understanding the psychological procedure behind user decisions, we present the behavior path and propose to match the user's current behavior path with historical behavior paths to predict user behaviors on the app. Further, we design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths. In particular, we leverage contrastive learning to augment user behavior paths, provide behavior path self-activation to alleviate the effect of noise, and adopt a two-level matching mechanism to identify the most appropriate candidate. Our model shows excellent performance on two real-world datasets, outperforming the state-of-the-art CTR model. Moreover, our model has been deployed on the Meituan food delivery platform and has accumulated 1.6% improvement in CTR and 1.8% improvement in advertising revenue.