Optimizing Recommendations with Multi-Armed & Contextual Bandits for Personalized Next Best Actions

Автор: WiDS Worldwide

Загружено: 2025-01-22

Просмотров: 673

Описание: In this WiDS Upskill Workshop, Keerthi Gopalakrishnan explores how Multi-Armed Bandit (MAB) and Contextual Bandit algorithms can optimize online recommendations for next-best-action scenarios. These techniques help balance the trade-off between exploration (trying new recommendations) and exploitation (leveraging successful actions) to drive better personalization and engagement.

Keerthi will break down key MAB concepts, including:

Epsilon-greedy
Upper Confidence Bound (UCB) & Contextual UCB
Thompson Sampling
Real-world applications in recommendation systems

This session is ideal for:

Data Scientists and Machine Learning Engineers
Product Managers and Data Strategists
Researchers and Academics

Prior knowledge: A background in supervised learning and evaluation metrics is recommended. Familiarity with online learning or decision-making algorithms is helpful but not required.

Learn more about WiDS Upskill Workshops: https://www.widsworldwide.org/learn/u...

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Optimizing Recommendations with Multi-Armed & Contextual Bandits for Personalized Next Best Actions

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