Personalised E-commerce Recommendation System

• Author(s):

  Mohamed Suhail J | Nilavanan S A | Dhanush B | Kailashwaran R | Dr.Palanivel S

• Keywords:

  Deep Q-Learning, Hyper-Personalization, Recommendation System, Sentiment Analysis, Reinforcement Learning, Reward Function, User Interaction

• Abstract:

  In The Competitive Landscape Of E-commerce, Providing Personalized Product Recommendations Is A Vital Yet Complex Challenge. Traditional Recommendation Systems Often Fail To Adapt To The Rapidly Changing Preferences Of Users, Resulting In Generic Suggestions And Diminished Customer Satisfaction. This Project Proposes A Cutting-edge Solution Leveraging Deep Reinforcement Learning (DRL) To Deliver Real-time, Personalized Recommendations. The System Dynamically Classifies Users Based On Interaction Patterns And Purchase Behavior, Allowing For Continual Learning And Adjustment To Individual Preferences. It Incorporates Techniques To Address Issues Such As Sparse Data And Recommendation Biases, Ensuring Fairness, Robustness, And Relevance. This Research Contributes To The Evolution Of Recommendation Technologies By Enhancing User Engagement, Increasing Customer Loyalty, And Setting New Standards For Personalized Digital Experiences In E-commerce Platforms.

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Other Details

• Paper id:

  IJSARTV11I4103246

• Published in:

  Volume: 11 Issue: 4 April 2025

• Publication Date:

  2025-04-21

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