# Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation

arXiv: https://arxiv.org/abs/2202.08959 · HTML: https://mlsanity.com/paper/2202.08959/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision](https://mlsanity.com/paper/1906.01620/) (2019)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [A Simple Convolutional Generative Network for Next Item Recommendation](https://mlsanity.com/paper/1808.05163/) (2018)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://mlsanity.com/paper/1904.08030/) (2019)
- [Deep Session Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.06482/) (2019)
- [Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction](https://mlsanity.com/paper/2007.06434/) (2020)
- [GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing](https://mlsanity.com/paper/1907.04433/) (2019)
- [SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios](https://mlsanity.com/paper/2110.06475/) (2021)
- [Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction](https://mlsanity.com/paper/2012.06968/) (2020)
- [Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System](https://mlsanity.com/paper/1805.09484/) (2018)

## Built on by (most cited first, 9 of 9)

- [Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search](https://mlsanity.com/paper/2304.02089/) (2023)
- [Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2401.07769/) (2024)
- [PHN: Parallel heterogeneous network with soft gating for CTR prediction](https://mlsanity.com/paper/2206.09184/) (2022)
- [Deep Intention-Aware Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2211.08650/) (2022)
- [Impression-Aware Recommender Systems](https://mlsanity.com/paper/2308.07857/) (2023)
- [Deep Multi-Representation Model for Click-Through Rate Prediction](https://mlsanity.com/paper/2210.10664/) (2022)
- [DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations](https://mlsanity.com/paper/2308.10527/) (2023)
- [PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction](https://mlsanity.com/paper/2403.10049/) (2024)
- [DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2602.13971/) (2026)

## Abstract (arXiv)

In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms. In this paper, we present a new recommendation problem, Trigger-Induced Recommendation (TIR), where users' instant interest can be explicitly induced with a trigger item and follow-up related target items are recommended accordingly. TIR has become ubiquitous and popular in e-commerce platforms. In this paper, we figure out that although existing recommendation models are effective in traditional recommendation scenarios by mining users' interests based on their massive historical behaviors, they are struggling in discovering users' instant interests in the TIR scenario due to the discrepancy between these scenarios, resulting in inferior performance. To tackle the problem, we propose a novel recommendation method named Deep Interest Highlight Network (DIHN) for Click-Through Rate (CTR) prediction in TIR scenarios. It has three main components including 1) User Intent Network (UIN), which responds to generate a precise probability score to predict user's intent on the trigger item; 2) Fusion Embedding Module (FEM), which adaptively fuses trigger item and target item embeddings based on the prediction from UIN; and (3) Hybrid Interest Extracting Module (HIEM), which can effectively highlight users' instant interest from their behaviors based on the result of FEM. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of DIHN over state-of-the-art methods.