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Hybrid AI Model Enhances E-commerce Customer Behavior Prediction

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a hybrid Ret-DNN with XGBoost model to improve customer behavior forecasting in e-commerce. This model combines a deep neural network for feature extraction with gradient boosting for prediction, utilizing data from a UK-based online retailer. The proposed model achieved a Mean Absolute Error of 0.2193, outperforming the existing Ret-DNN model. AI

IMPACT This hybrid model offers improved accuracy for e-commerce platforms seeking to understand and predict customer purchasing behavior.

RANK_REASON The cluster contains an academic paper detailing a new AI model for a specific application.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Degala Pushpa Sri, Mayank Atreya, Lakshmi. H, Navin Chhibber, Mukesh Soni ·

    Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

    arXiv:2606.17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online. However, retail platforms have struggled to understand customer behavior and make it…

  2. arXiv cs.LG TIER_1 English(EN) · Mukesh Soni ·

    Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

    In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online. However, retail platforms have struggled to understand customer behavior and make it difficult to predict their future purchases. To…

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