CTR Prediction on Alibaba's Taobao Advertising Dataset Using Traditional and Deep Learning Models

TL;DR

This paper explores advanced modeling techniques, including deep learning and Transformer architectures, to improve click-through rate prediction accuracy on Alibaba's large-scale Taobao dataset, emphasizing temporal user behavior.

Contribution

It introduces a Transformer-based model for CTR prediction that captures temporal dynamics and user interest evolution, outperforming traditional models.

Findings

Transformer improves AUC by 2.81% over baseline

Deep learning models better capture user behavior patterns

Temporal modeling enhances prediction for diverse users

Abstract

Click-through rates prediction is critical in modern advertising systems, where ranking relevance and user engagement directly impact platform efficiency and business value. In this project, we explore how to model CTR more effectively using a large-scale Taobao dataset released by Alibaba. We start with supervised learning models, including logistic regression and Light-GBM, that are trained on static features such as user demographics, ad attributes, and contextual metadata. These models provide fast, interpretable benchmarks, but have limited capabilities to capture patterns of behavior that drive clicks. To better model user intent, we combined behavioral data from hundreds of millions of interactions over a 22-day period. By extracting and encoding user action sequences, we construct representations of user interests over time. We use deep learning models to fuse behavioral embeddings with static features. Among them, multilayer perceptrons (MLPs) have achieved significant performance improvements. To capture temporal dynamics, we designed a Transformer-based architecture that uses a self-attention mechanism to learn contextual dependencies across behavioral sequences, modeling not only what the user interacts with, but also the timing and frequency of interactions. Transformer improves AUC by 2.81 % over the baseline (LR model), with the largest gains observed for users whose interests are diverse or change over time. In addition to modeling, we propose an A/B testing strategy for real-world evaluation. We also think about the broader implications: personalized ad targeting technology can be applied to public health scenarios to achieve precise delivery of health information or behavior guidance. Our research provides a roadmap for advancing click-through rate predictions and extending their value beyond e-commerce.