Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks (1404.5772v3)

Abstract: 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.

Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks

The paper "Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks" by Zhang et al. introduces an innovative approach in the domain of click prediction in sponsored search systems. The authors apply Recurrent Neural Networks (RNNs) to model the sequential dependencies inherent in user behavior, an aspect traditionally overlooked by independent event-based models.

Introduction

Sponsored search is a critical revenue model for search engines, in which estimating the Click-Through Rate (CTR) of advertisements is imperative. Before this paper, CTR prediction largely involved treating ad impressions as isolated events, neglecting the sequential behavior of users. This paper challenges that paradigm by considering temporal sequences using RNNs, leveraging their ability to manage dependencies across time-related data.

Key Contributions

The paper makes three significant contributions:

1. Identification of Sequential Dependencies: The authors analyze user interaction logs to identify temporal dependencies in user behavior concerning ad impression sequences. Key insights, such as the negative impact of "quick back" clicks (where users quickly exit ad pages), guide their modeling approach.
2. RNN Application for Sequential Modeling: The work leverages RNNs to embed these sequential dependencies into the click prediction framework. As users' interactions are sequentially processed by the RNN, the hidden recurrent layers capture the dynamic dependencies between events, enhancing prediction accuracy.
3. Empirical Validation through Large-Scale Evaluation: Extensive experiments on data from a commercial search engine validate the proposed model's efficacy. The RNN-based approach demonstrates superior performance in comparison to traditional models like Logistic Regression and conventional Neural Networks, with notable improvements in metrics such as AUC and Relative Information Gain (RIG).

Methodology

The methodology section outlines the innovative use of RNNs to model sequential user data in sponsored search. Inputs such as ad features, user features, and temporal data are structured as sequences for the RNN model. The training employs Back Propagation Through Time (BPTT) to address the intrinsic challenges of learning from sequential data, ensuring that both short-span and long-span dependencies are captured.

Results and Implications

The experimental results are telling. The RNN model consistently outperforms baseline models across various ad positions, exhibiting a relative gain in RIG of about 17.3% over Logistic Regression and 10% over standard Neural Networks. This improvement underscores the critical role of temporal dependencies in click prediction. The paper provides a strong basis for future research into sequential modeling techniques in online advertising and AI.

Future Directions

The authors suggest several avenues for further exploration. These include refining sequence-building strategies by considering different levels of sequence granularity, such as user-ad pairs or broader system-level sequences, and enhancing deep structural understanding through model interpretability studies. Additionally, they propose investigating the potential of Deep Recurrent Neural Networks (DRNN) to further exploit the benefits of hierarchical and recurrent structures in modeling complex sequential dependencies.

Conclusion

This paper's approach marks a substantial step forward in click prediction methodologies by effectively integrating sequential learning frameworks such as RNNs into the analysis of user behavior in sponsored search. This integration enhances prediction accuracy and provides deeper insights into user interaction patterns, paving the way for optimizing revenue models and user experiences in digital advertising ecosystems.