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Monday, August 17 • 11:00am - 11:40am
Un-collaborative filtering: Giving the right recommendations when your users aren’t helping you

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Competitions, such as the Netflix Prize and Kaggle, have driven a great deal of research on recommendation engines. However, many e-commerce datasets lack explicit ratings, consisting solely of binary purchase information containing no labeled negative data. Such data requires special consideration and treatment for both model selection and validation of results. In this talk I will describe implementation of a recommendation system for binary purchase data in Spark’s MLlib, compare fitting and prediction benchmarks for various models, and illustrate the performance differences across different scales of big data. Finally, I will share the lessons learned in how to efficiently select and implement the best recommendation model for your dataset.

Speakers
avatar for Leah McGuire

Leah McGuire

Senior Member of Technical Staff, Salesforce
Leah McGuire is a Senior Member of Technical Staff at Salesforce, implementing data-driven features and recommendations in Salesforce products. Before joining Salesforce, Leah was a Senior Data Scientist on the data products team at LinkedIn working on personalization, entity resolution, and relevance for a variety of LinkedIn data products. She completed a PhD and a Postdoctoral Fellowship in Computational Neuroscience at the University of... Read More →


Monday August 17, 2015 11:00am - 11:40am
Track B

Attendees (14)