Predicting Purchase Intent from E-Commerce Behavior Sequences

DOI

10.2991/978-2-38476-585-0_38How to use a DOI?

Keywords

Multilayer Perceptron; Predicting Purchase Intent; E-Commerce Behavior Sequences

Abstract

This paper investigates the problem of predicting purchase intent based on early-stage user interaction sequences in e-commerce browsing sessions. The task is formulated as a binary classification problem, aiming to determine whether a purchase will occur using only the first three events of each session. Several modeling approaches are compared, including logistic regression, random forest, multilayer perceptron (MLP), and the Neural Attentive Recommendation Machine (NARM)—a deep sequential model that integrates gated recurrent units with attention mechanisms. A publicly available dataset from a multi-category online retailer is used to extract both aggregated session-level features and item-level behavioral sequences. Evaluation results show that NARM achieves the highest AUC (0.867) and F1 score (0.725), outperforming classical models even with truncated input. Interpretability is supported through feature importance analysis in classical models and attention heatmaps in NARM, revealing how different user behaviors contribute to predictions. These results underscore the effectiveness of sequence-aware modeling for real-time purchase intent prediction and demonstrate the complementary value of interpretable explanations in commercial applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zeyu Shen
PY  - 2026
DA  - 2026/06/18
TI  - Predicting Purchase Intent from E-Commerce Behavior Sequences
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 322
EP  - 333
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_38
DO  - 10.2991/978-2-38476-585-0_38
ID  - Shen2026
ER  -