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

arXiv: https://arxiv.org/abs/2206.00510 · HTML: https://mlsanity.com/paper/2206.00510/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2022.

## Built on by (most cited first, 5 of 5)

- [Causality-based CTR Prediction using Graph Neural Networks](https://mlsanity.com/paper/2301.12762/) (2023)
- [HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer](https://mlsanity.com/paper/2305.17386/) (2023)
- [DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction](https://mlsanity.com/paper/2305.04891/) (2023)
- [Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification](https://mlsanity.com/paper/2308.05385/) (2023)
- [PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction](https://mlsanity.com/paper/2403.10049/) (2024)

## Abstract (arXiv)

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.