Speaker: Le Duy Dung, Assistant Professor, College of Engineering and Computer Science

Abstract

Personalized recommendation, whose objective is to generate a limited list of items (e.g., products on Amazon, movies on Netflix, or pins on Pinterest, etc.) for each user, has gained extensive attention from both researchers and practitioners in the last decade. The necessity of personalized recommendation is driven by the explosion of available options online, which makes it difficult, if not downright impossible, for each user to investigate every option. Product and service providers rely on recommendation algorithms to identify manageable number of the most likely or preferred options to be presented to each user. Also, due to the limited screen estate of computing devices, this manageable number maybe relatively small, yet the selection of items to be recommended is personalized to each individual users.

In this talk, we will review several classic and modern machine learning approaches to building personalized recommender systems. We will also discuss several emerging variants of recommender systems such as recommendations in multi-sided marketplaces, privacy-aware personalization, etc. and other factors of recommender systems that are beyond machine learning algorithms.