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Muralidhar nikhil multi_armed_bandits

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High-Level introduction about multi-armed-bandit approach for content testing and how to map the MAB approach for Article Recommendation.

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Muralidhar nikhil multi_armed_bandits

  1. 1. Multi Armed Bandits For Article Recommendation By: Nikhil Muralidhar
  2. 2. Overview • Content Testing • Multi Armed Bandits (MAB) • Article Recommendation – Motivation – Challenges – Goals • MAB for Article Recommendation • Personalization with MAB
  3. 3. Content Testing
  4. 4. Multi-Armed Bandits
  5. 5. Article Recommendation Motivation
  6. 6. Article Recommendation Challenges 1. Implicit Feedback 2. Temporal Sensitivity of Content 1. Highly Dynamic Article Universe 2. Each reader consumes a very small subset of content.
  7. 7. Article Recommendation Goals – Focus on Article Performance – Continuously incorporate new stories – Discard old unpopular stories – Assumption: Clicks are a proxy to user engagement.  Maximize Clicks (exploit)  Serve variegated content (explore)
  8. 8. MAB for Article Recommendation 0.6 0.3 0.2 0.8 Explore Exploit
  9. 9. MAB for Article Recommendation • Each Article is an arm. • Example arm a: – served as – clicked ac – reward = 1.0 – performance = (ac / as)*reward – confidence = sum(all arms served)/as – ascore = confidence + performance • New articles organically incorporated • Unpopular Articles have low ascore
  10. 10. Personalization with MAB • Easy to incorporate user specific metrics. – confidence = sum(all arms served) /as – ascore = confidence + performance + .. • Article Freshness • Reader Interest • Concurrent Users • Popularity on Social Media
  11. 11. Questions?

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