This is to Livestream the talk event hosted by Bay Area Machine learning group at Intel.

Recommendation systems are vital to keeping users engaged and satisfied with personalized recommendations in the age of information explosion. Users expect personalized content in modern E-commerce, entertainment and social media platforms but the effectiveness of recommendations are restricted by existing user-item interactions and model capacity.
The ability to leverage higher order reasoning may help alleviate the problem of sparsity. On the other hand, deep learning has made massive strides in many research areas obtaining state of the art performance in computer vision, speech recognition, and natural language processing.

In this talk, I will present the recent successful integration of deep learning methods in recommendation systems that demonstrates the noticeable advantages of complex nonlinear transformations and tackling challenging cold-start problems over traditional recommendation techniques.