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Recommendations and Discovery at StumbleUpon

by Engineering Director at StumbleUpon Inc on Sep 12, 2012

  • 19,792 views

RecSys 2012 Industry Track - Sumanth Kolar, StumbleUpon ...

RecSys 2012 Industry Track - Sumanth Kolar, StumbleUpon

It's human nature to be curious, to learn new things, to want to find out more. Discovery is an innate human need, and with the rise of the Web, the urge to learn more has increased by leaps and bounds. According to David Hornik, investor at August Capital, "The massive scale of the Web not only creates huge challenges for search, it also cripples discovery. Gone are the good old days in which fortuity would lead to the unearthing of interesting new websites." Indeed, we live in the age of "infovores" and there is definitely a need for a service that provides serendipity.

Providing serendipitous discovery that can inform, entertain and enlighten our users is of utmost importance to StumbleUpon. This talk will focus on how StumbleUpon uses several machine learning techniques such as collaborative filtering techniques, active learning, decision trees, Bayesian models and more to solve complex problems involving classification, user behavior analysis, modelling, anti-spam and recommendations. An average StumbleUpon user spends over 7 hours per month using the product, equating to hundreds of varied recommendations and ample feedback. The talk will also provide insights into some of StumbleUpon's rich data and how we can use scale to accomplish what would otherwise not be possible. We will look at innovative ways that StumbleUpon figures out the right metrics to evaluate recommender systems - a very complex problem. We will also discuss our research on StumbleUpon's mobile activity, which is growing 800% year over year and is the fastest growing part of our business, and how mobile recommendations are unique and important.

Bio: As Engineering Director at StumbleUpon, Sumanth Kolar leads the applied research team, overseeing recommendations, anti-spam, content analysis, user modeling, data sciences and infrastructure. ?Sumanth tackles very interesting and challenging research problems as StumbleUpon delivers more than 1 billion personalized recommendations a month to its more than 25 million users. Prior to joining the company in 2009, Sumanth engineered bidding and computer vision systems at Yahoo! and Adobe Research. Sumanth holds a masters degree in computer science from the University of California at Santa Cruz.

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Recommendations and Discovery at StumbleUpon Recommendations and Discovery at StumbleUpon Presentation Transcript