# Deep Intention-Aware Network for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/2211.08650 · HTML: https://mlsanity.com/paper/2211.08650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 12 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Deep Session Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.06482/) (2019)
- [Learning a Product Relevance Model from Click-Through Data in E-Commerce](https://mlsanity.com/paper/2102.07098/) (2021)
- [Adversarial Multimodal Representation Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2003.07162/) (2020)
- [Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation](https://mlsanity.com/paper/2206.05020/) (2022)
- [Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2202.08959/) (2022)
- [GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction](https://mlsanity.com/paper/2202.11525/) (2022)

## Built on by (most cited first, 1 of 1)

- [AI Can Enhance Creativity in Social Networks](https://mlsanity.com/paper/2410.15264/) (2024)

## Abstract (arXiv)

E-commerce platforms provide entrances for customers to enter mini-apps that can meet their specific shopping requirements. Trigger items displayed on entrance icons can attract more entering. However, conventional Click-Through-Rate (CTR) prediction models, which ignore user instant interest in trigger item, fail to be applied to the new recommendation scenario dubbed Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high stickiness of customers to mini-apps, we argue that existing trigger-based methods that over-emphasize the importance of trigger items, are undesired for TIRA, since a large portion of customer entries are because of their routine shopping habits instead of triggers. We identify that the key to TIRA is to extract customers' personalized entering intention and weigh the impact of triggers based on this intention. To achieve this goal, we convert CTR prediction for TIRA into a separate estimation form, and present Deep Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that estimates user's entering intention, i.e., whether he/she is affected by the trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that estimate CTRs given user's intention is to the trigger-item and the mini-app respectively. Following a joint learning way, DIAN can both accurately predict user intention and dynamically balance the results of trigger-free and trigger-based recommendations based on the estimated intention. Experiments show that DIAN advances state-of-the-art performance in a large real-world dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for Juhuasuan, a famous mini-app of Taobao.