Mastering Contextual Bandits: Personalization and Decision-Making in Real-Time

Author(s): Joseph Robinson, Ph.D.

Originally published on Towards AI.

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Contextual bandits extend traditional multi-armed bandit algorithms by incorporating contextual data to make more personalized, adaptive decisions.

This blog explores the theory of contextual bandits, their applications, and their implementation, demonstrating how they revolutionize real-time decision-making in recommendations, dynamic pricing, and advertising.

· Introduction· Introduction to Contextual Bandits ∘ What are Contextual Bandits? ∘ Key Differences: Multi-Armed Bandits (MABs) vs. Contextual Bandits ∘ Key Components of Contextual Bandits ∘ Exploration vs. Exploitation ∘ Simple Use Cases of Contextual Bandits· Mathematical Formulation of Contextual Bandits ∘ Python Code Example for Contextual Bandits· Contextual Bandits Approaches ∘ Problem Formulation ∘ Thompson Sampling for Contextual Bandits ∘ LinUCB (Linear Upper Confidence Bound)· Real-World Applications of Contextual Bandits ∘ Personalization: E-commerce and Content Recommendation Engines ∘ Dynamic Pricing: Airlines, Hotels, and Ride-Sharing Platforms ∘ Balancing Ad Placements Based on User Behavior and Feedback ∘ Python Code Example: Contextual Bandits in Advertising· Conclusion· References· Call to Action

Imagine choosing which ad to show every visitor on a high-traffic website. Each click costs money, and each missed opportunity means potential revenue lost. How do you optimize your decision-making? This is where contextual bandits come into play—a powerful approach… Read the full blog for free on Medium.

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Published via Towards AI