- Abhi Yadav - CEO at DataXylo (Linkedin)
- Apparao Kari - CEO at Cintell (Linkedin)
- Rag Srinivas - Cloud Architect at IBM (Linkedin)
- Snejina Zacharia - CEO at Insurify (Twitter - Linkedin)
There is an important aspect of practical recommender systems that we noticed while competing in the ICML Exploration-Exploitation 3 data mining competition. The goal of the competition was to build a better recommender system for Yahoo!'s Front Page, which provides personalized new article recommendations. The main strategy we used was to carefully control the balance between exploiting good articles and exploring new ones in the multi-armed bandit setting. This strategy was based on our observation that there were clear trends over time in the click-through-rates of the articles. At certain times, we should explore new articles more often, and at certain times, we should reduce exploration and just show the best articles available. This led to dramatic performance improvements. As it turns out, the observation we made in the Yahoo! data is in fact pervasive in settings where recommender systems are currently used...
Cynthia Rudin is an associate professor of statistics at the Massachusetts Institute of Technology associated with the Computer Science and Artificial Intelligence Laboratory and the Sloan School of Management, and directs the Prediction Analysis Lab. Her interests are in machine learning, data mining, applied statistics, and knowledge discovery (Big Data). Her application areas are in energy grid reliability, healthcare, and computational criminology.