# Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1404.5772 · HTML: https://mlsanity.com/paper/1404.5772/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2014.

## Built on by (most cited first, 21 of 21)

- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data](https://mlsanity.com/paper/2310.13818/) (2023)
- [Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation](https://mlsanity.com/paper/2109.11790/) (2021)
- [Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification](https://mlsanity.com/paper/2112.15285/) (2021)
- [Aspect-based Analysis of Advertising Appeals for Search Engine Advertising](https://mlsanity.com/paper/2204.11445/) (2022)
- [Boost CTR Prediction for New Advertisements via Modeling Visual Content](https://mlsanity.com/paper/2209.11727/) (2022)
- [A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations](https://mlsanity.com/paper/2204.00970/) (2022)
- [STAR: A Session-Based Time-Aware Recommender System](https://mlsanity.com/paper/2211.06394/) (2022)
- [Self Contrastive Learning for Session-based Recommendation](https://mlsanity.com/paper/2306.01266/) (2023)
- [REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction over Sparse Trajectories](https://mlsanity.com/paper/2402.16310/) (2024)
- [Learning to Learn a Cold-start Sequential Recommender](https://mlsanity.com/paper/2110.09083/) (2021)
- [On the Overlooked Significance of Underutilized Contextual Features in Recent News Recommendation Models](https://mlsanity.com/paper/2112.14370/) (2021)
- [Sequential Search with Off-Policy Reinforcement Learning](https://mlsanity.com/paper/2202.00245/) (2022)
- [Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition](https://mlsanity.com/paper/2205.11771/) (2022)
- [Enhancing Next Destination Prediction: A Novel Long Short-Term Memory Neural Network Approach Using Real-World Airline Data](https://mlsanity.com/paper/2401.12830/) (2024)
- [User-click Modelling for Predicting Purchase Intent](https://mlsanity.com/paper/2112.02006/) (2021)
- [Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification](https://mlsanity.com/paper/2201.00014/) (2021)
- [Learning Service Selection Decision Making Behaviors During Scientific Workflow Development](https://mlsanity.com/paper/2404.00420/) (2024)
- [Ranking Policy Learning via Marketplace Expected Value Estimation From Observational Data](https://mlsanity.com/paper/2410.04568/) (2024)

## Abstract (arXiv)

Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user's behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what queries she submitted, what ads she clicked or ignored, and how long she spent on the landing pages of clicked ads, etc. Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN). Compared to traditional methods, this framework directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that our approach can significantly improve the click prediction accuracy, compared to sequence-independent approaches.