Shipra Agrawal | Explore and Exploit: Because You May Not Know What You're Missing
To improve its movie recommendations to subscribers, Netflix looks at what subscribers liked in the past to predict future preferences. But that method leaves out movies subscribers might like even better but don’t know about. Amazon faces a similar problem in recommending products to its customers. Discovering the full range of possibilities involves a trade-off between exploration and exploitation of data. Many sequential decision making problems are rooted in this problem, including recommendation systems, online advertising, content optimization, revenue and inventory management, and even teaching computers to play games like Pong and Go. I will discuss how machine learning and optimization techniques can be combined to achieve near-optimal trade-offs between exploration and exploitation.
Olivier Toubia | Recommending Movies by Character Traits Featured
Current movie recommendation systems are largely based on viewers’ past preferences. We propose an alternative that taps into viewer preferences for stories that feature specific character traits, a finding documented in the media psychology literature. Borrowing from the positive psychology literature, we have developed a character-based classification system that is easy to interpret, communicate and act on. We have also developed a companion natural language processing tool that can infer character traits from movie summaries. In two online studies, we show that character traits are a strong predictor of what movies people like. Our results apply to films that achieve critical acclaim as well as box-office success. We show that character-based classification works for models that use content alone, and content with collaborative filtering, to predict viewer behavior.