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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

DEEP LEARNING FOR E‑COMMERCE RECOMMENDATIONS: CAPTURING LONG- AND SHORT-TERM USER PREFERENCES WITH CNN-BASED REPRESENTATION LEARNING

DOI https://doi.org/10.55640/ijidml-v02i08-02
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Qi Xin 1 ,
4 Management Information Systems University of Pittsburgh, PA, USA

Abstract

Recommender systems are crucial in e-commerce for matching users with products of interest. This paper proposes a deep learning approach that leverages representation learning to capture complex patterns in user behavior, combining long-term preferences with short-term, session-based interests. We develop a neural network model that integrates Convolutional Neural Networks (CNNs) for sequential pattern extraction with user embedding vectors representing historical preferences. Experiments are conducted on open e-commerce datasets with implicit (click/view) and explicit (purchase) feedback. The proposed model outperforms baseline recommendation techniques, achieving higher Hit Rate (HR@10) and Normalized Discounted Cumulative Gain (NDCG@10). The results demonstrate that blending users’ stable long-term tastes with their recent short-term actions leads to more accurate recommendations. This work reinforces deep learning as a powerful tool for representation learning in recommender systems, and our findings offer insights into modeling nuanced user behaviors for intelligent data-driven recommendations in online retail.

Keywords

Recommender Systems, Deep Learning, Representation Learning, Convolutional Neural Networks, E-Commerce, User Preferences

References

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