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Recommender Engines : A Peak into Predictive Analytics

Recommender Engines : A Peak into Predictive Analytics

Proposed talk on Predictive Analytics and Recommender Engines

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Raghav Bali

June 12, 2016
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Transcript

  1. Recommender Engines A Peak into Predictive Analytics

  2. Predictive Analytics http://giphy.com/gifs/season-6-the-simpsons-6x19-3orieSdZDhn7I6gViw

  3. Predictive Analytics • Analysis of current and historical facts/data to

    make predictions about the future • Traditionally a field of statistics/statistical computing. • Now encompasses machine learning and data mining. Current Data Historical Data Predict Future Machine Learning / Statistics
  4. Analytical Maturity

  5. Analytical Maturity

  6. Recommender Engines • Class of Information Filtering systems • Model

    user preferences • Analyse input data to predict output similar to user preferences.
  7. Types of RE • Collaborative Filters • Content Based Filters

    • Hybrid Recommender Engines http://i.imgur.com/xlXjtOL.jpg
  8. RE: Collaborative Filters • Also termed as User Based CF

    • Users with similar behaviours and/or attributes have similar preferences
  9. RE : Content Based • Also termed as Item Based

    CD+F • Item attributes along with user personas are utilized to build preference models
  10. RE : Hybrid • Best of both worlds • Can

    be modelled using User Based CF and Item Based CF in different configurations. • Less prone to issues of sparsity and cold start.
  11. Quick and Dirty RE • Matrix Factorization based Recommender Engine

  12. Quick and Dirty RE • Code and Results

  13. Applications • Jobs you may be interested in • Who

    to follow • Other movies you might enjoy
  14. Issues • Cold Start Problem • Sparsity Problem • Filter

    Bubble http://ebiquity.umbc.edu/blogger/2015/06/08/hot-stuff-at-coldstart/
  15. References • R Machine Learning by Example (link) • Gartner

    Analytics Maturity Model (link)
  16. THANK YOU Raghav Bali (@rghv_bali) http://xkcd.org/892/