# Deep Session Interest Network for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/1905.06482 · HTML: https://mlsanity.com/paper/1905.06482/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 30 of 64)

- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://mlsanity.com/paper/2108.04468/) (2021)
- [Deep Learning for Click-Through Rate Estimation](https://mlsanity.com/paper/2104.10584/) (2021)
- [A Survey on User Behavior Modeling in Recommender Systems](https://mlsanity.com/paper/2302.11087/) (2023)
- [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://mlsanity.com/paper/2205.10249/) (2022)
- [SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios](https://mlsanity.com/paper/2110.06475/) (2021)
- [Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model](https://mlsanity.com/paper/2208.06164/) (2022)
- [Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction](https://mlsanity.com/paper/2104.09713/) (2021)
- [Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction](https://mlsanity.com/paper/2106.02768/) (2021)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer](https://mlsanity.com/paper/2201.13311/) (2022)
- [Ads Recommendation in a Collapsed and Entangled World](https://mlsanity.com/paper/2403.00793/) (2024)
- [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://mlsanity.com/paper/2308.14029/) (2023)
- [Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2202.08959/) (2022)
- [Temporal Interest Network for User Response Prediction](https://mlsanity.com/paper/2308.08487/) (2023)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [PURS: Personalized Unexpected Recommender System for Improving User Satisfaction](https://mlsanity.com/paper/2106.02771/) (2021)
- [Denoising User-aware Memory Network for Recommendation](https://mlsanity.com/paper/2107.05474/) (2021)
- [Long-Sequence Recommendation Models Need Decoupled Embeddings](https://mlsanity.com/paper/2410.02604/) (2024)
- [Multi-Objective Personalized Product Retrieval in Taobao Search](https://mlsanity.com/paper/2210.04170/) (2022)
- [DCAP: Deep Cross Attentional Product Network for User Response Prediction](https://mlsanity.com/paper/2105.08649/) (2021)
- [Boost CTR Prediction for New Advertisements via Modeling Visual Content](https://mlsanity.com/paper/2209.11727/) (2022)
- [Deep Task-specific Bottom Representation Network for Multi-Task Recommendation](https://mlsanity.com/paper/2308.05996/) (2023)
- [Dynamic Sequential Graph Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2109.12541/) (2021)
- [Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction](https://mlsanity.com/paper/2112.11136/) (2021)
- [PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions](https://mlsanity.com/paper/2209.13015/) (2022)
- [Decision-Making Context Interaction Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2301.12402/) (2023)
- [When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?](https://mlsanity.com/paper/2305.01801/) (2023)
- [Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models](https://mlsanity.com/paper/2412.04107/) (2024)
- [AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2211.12105/) (2022)

## Abstract (arXiv)

Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and evolving interests from their behavior sequences remains a continuous research topic in the CTR prediction. However, most existing studies overlook the intrinsic structure of the sequences: the sequences are composed of sessions, where sessions are user behaviors separated by their occurring time. We observe that user behaviors are highly homogeneous in each session, and heterogeneous cross sessions. Based on this observation, we propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages users' multiple historical sessions in their behavior sequences. We first use self-attention mechanism with bias encoding to extract users' interests in each session. Then we apply Bi-LSTM to model how users' interests evolve and interact among sessions. Finally, we employ the local activation unit to adaptively learn the influences of various session interests on the target item. Experiments are conducted on both advertising and production recommender datasets and DSIN outperforms other state-of-the-art models on both datasets.