HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction

TL;DR

This paper introduces HIEN, a hierarchical embedding network that models attribute dependencies and user/item intents for improved click-through rate prediction, addressing limitations of traditional feature and interest modeling methods.

Contribution

The paper proposes a novel hierarchical intention embedding network that captures attribute dependencies and user/item intents, enhancing CTR prediction accuracy and interpretability.

Findings

HIEN outperforms state-of-the-art methods on public and production datasets.

HIEN improves existing CTR models when used as an input module.

The model demonstrates significant performance gains in real-world systems.

Abstract

Click-through rate (CTR) prediction plays an important role in online advertising and recommendation systems, which aims at estimating the probability of a user clicking on a specific item. Feature interaction modeling and user interest modeling methods are two popular domains in CTR prediction, and they have been studied extensively in recent years. However, these methods still suffer from two limitations. First, traditional methods regard item attributes as ID features, while neglecting structure information and relation dependencies among attributes. Second, when mining user interests from user-item interactions, current models ignore user intents and item intents for different attributes, which lacks interpretability. Based on this observation, in this paper, we propose a novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph. HIEN also captures user intents for different item attributes as well as item intents based on our proposed hierarchical attention mechanism. Extensive experiments on both public and production datasets show that the proposed model significantly outperforms the state-of-the-art methods. In addition, HIEN can be applied as an input module to state-of-the-art CTR prediction methods, bringing further performance lift for these existing models that might already be intensively used in real systems.