๐Ÿ‡บ๐Ÿ‡ธBayesian Networks for Recommender Systems: Going Beyond Ratings Prediction

Michael L. Thompson, Ph.D.,

Presented at the 8th Annual BayesiaLab Conference on October 27, 2020.


Recommender systems are some of the most useful business applications built using Machine Learning. In our talk, we demonstrate how to build a recommender system for movies using Bayesian Machine Learning. Moreover, the unique features of BayesiaLab, like โ€œMost Relevant Explanationโ€ and โ€œEvidence Instantiationโ€, allow us to extend the recommender system so we can gain insights into the audiences of each movie. Yet, we ask for more! We suggest extensions to BayesiaLabโ€™s already powerful feature set.

Presentation Video

Presentation Slides

About the Presenter

Dr. Michael L. Thompson is retired from the Procter & Gamble Company, where he led Bayesian Analysis R&D in consumer & market modeling. His degrees are in Chemical Engineering: B.S., Northwestern University, โ€™82; M.S., MIT, โ€™84; and Ph.D., MIT, โ€™96, with a minor in Statistics and Artificial Intelligence. Michael has extensive experience in the process industry, having worked for Dow, Alcoa, Amoco, and Mitsubishi Chemical (Japan). At P&G for 21 years, Michael applied his expertise in Bayesian Analysis, especially Bayesian belief networks (BBN), to deliver results in the consumer-packaged goods (CPG) industry. His contributions spanned business functions, including R&D, Engineering, Manufacturing, Marketing, and Business Analytics. He has authored journal articles ranging from fluidized bed reactors to hybrid probabilistic and first-principles biochemical models to optimal consumer product design. Currently, Michael is a Term Adjunct in the Lindner College of Business at the University of Cincinnati, where he teaches Bayesian Analysis to candidates for the Master of Science in Business Analytics. He also serves on the Advisory Board for the Retail AI Lab of the Northwestern University Retail Analytics Council.

Previous Conference Presentations

๐Ÿ‡บ๐Ÿ‡ธpageUnderstanding Your Customer Through the 'Most Relevant Explanations' (MRE) Function in BayesiaLab
  • Bayesian Sense-Making in Data Science (Chicago, 2018)

  • Bayesian, Bayesia, BayesiaLab โ€ฆ and P&G (Orlando, 2013)

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