Predicting E-commerce Purchase Behavior using a DQN-Inspired Deep Learning Model for enhanced adaptability
Keywords:
Deep learning, e-commerce, predictive models, Deep Q-Networks, LSTM, user behavior analysis, machine learning, neural networks, reinforcement learning, time series analysis, data mining, big data, recommender systems, customer relationship management, demand forecastingAbstract
This paper presents a novel approach to predicting buying intent and product demand in e-commerce settings, leveraging a Deep Q-Network (DQN) inspired architecture. In the rapidly evolving landscape of online retail, accurate prediction of user behavior is crucial for optimizing inventory management, personalizing user experiences, and maximizing sales. Our method adapts concepts from reinforcement learning to a supervised learning context, combining the sequential modeling capabilities of Long Short-Term Memory (LSTM) networks with the strategic decision making aspects of DQNs.
We evaluate our model on a large scale ecommerce dataset comprising over 885,000 user sessions, each characterized by 1,114 features. Our approach demonstrates robust performance in handling the inherent class imbalance typical in e-commerce data, where purchase events are significantly less frequent than non-purchase events. Through comprehensive experimentation with various classification thresholds, we show that our model achieves a balance between precision and recall, with an overall accuracy of 88% and an AUC-ROC score of 0.88.
Comparative analysis reveals that our DQN-inspired model offers advantages over traditional machine learning and standard deep learning approaches, particularly in its ability to capture complex temporal patterns in user behavior. The model’s performance and scalability make it well suited for real world e-commerce applications dealing with high dimensional, sequential data.
This research contributes to the field of e-commerce analytics by introducing a novel predictive modelling technique that combines the strengths of deep learning and reinforcement learning paradigms. Our findings have significant implications for improving demand forecasting, personalizing user experiences, and optimizing marketing strategies in online retail environments.
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References
eMarketer. (2023) Worldwide ecommerce forecast 2023. eMarketer. [Online]. Available: https://www.emarketer.com/content/worldwide-ecommerce-forecast-2023
C.-H. Lo, D. Frankowski, and J. Leskovec, “Machine learning with big data: Challenges and approaches,” IEEE Access, vol. 4, pp. 7776–7797, 2016.
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Computing Surveys (CSUR), vol. 52, no. 1, pp. 1–38, 2018.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Ve-ness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735– 1780, 1997.
S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques,” Emerging artificial intelligence applications in computer engineering, vol. 160, pp. 3–24, 2007.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” Proceedings of the 22nd acm sided international conference on knowledge discovery and data mining, pp. 785–794, 2016. IJISAE, 2025, 13(1s), 45-56 | 56International Journal of Intelligent Systems and Applications in Engineering
Y. Wu, J. Lim, and M.-H. Yang, “Recurrent neural network for (un-) supervised learning of monocular video visual odometry and depth,” arXiv preprint arXiv:1709.07050, 2017.
D. M. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011.
N. V. Chawla, “Data mining for imbalanced datasets: An overview,” Data mining and knowledge discovery handbook, pp. 875–886, 2009.
J. Davis and M. Goadrich, “The relationship between precision-recall and roc curves,” Proceedings of the 23rd international conference on Machine learning, pp. 233– 240, 2006.
Y. Sasaki et al., “The truth of the f-measure,” in Teach Tutor mater, vol. 1, no. 5, 2007, pp. 1–5.
T. Fawcett, “An introduction to roc analysis,” Pattern recognition letters, vol. 27, no. 8, pp. 861–874, 2006.
S. Visa, B. Ramsay, A. Ralescu, and E. Van Der Knaap, “Confusion matrix-based feature selection,” Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference, pp. 120–127, 2011.
X. Lin, X. Wang, and N. Hajli, “Predicting online purchasing behavior based on browsing history,” International Journal of Business and Management, vol. 12, no. 10, pp. 218–230, 2017.
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