Gal Lavee: "Recommendation Systems"

Recommendation systems are ubiquitous in our day to day lives. This domain lies at the intersection of academic and industry interests, with examples of fruitful collaborations abounding. In this talk I will motivate the field of automated recommendation systems and give short introduction to common approaches including content-based and Matrix Factorization for collaborative filtering. I will also discuss evaluation methods for recommendation algorithm results.

I will conclude with a description of and results from recent published work on integrating side information, namely user web browsing interaction, to improve Matrix Factorization based recommendation.