Predicting User Purchase Intent: Deep Contextual Sequence Modeling for E-Commerce Platforms

Abstract

Online retail environments are constantly evolving, generating rich streams of user interaction data in the process. Predicting which users are likely to transition from casual browsing to making an actual purchase has become a critical factor in crafting targeted marketing, dynamic pricing, and personalized shopping experiences. This paper delves into the use of deep contextual sequence modeling to forecast purchase intent on e-commerce platforms. By drawing on state-of-the-art architectures such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Transformers—combined with attention mechanisms—our approach captures both short-term user actions and longer-term patterns across multiple sessions. We enhance these models by embedding contextual information at the product level, encoding product metadata and user history more effectively. After an in-depth review of existing literature, we introduce a hybrid system that leverages attention-based components along with product embeddings to achieve robust user purchase intent predictions. Our experiments, conducted on a large public dataset, underscore significant gains in accuracy and F1 scores when compared to simpler methods and other sequence-based baselines. A comparative analysis provides further evidence of the system’s strengths, illustrated by tables and charts that detail performance benchmarks. Ultimately, this research demonstrates how integrating contextualized deep sequence models can yield impactful and precise predictions, helping online retailers better address user needs in real time.
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