Big Data Recommendation Systems

Speaker:  Samee U Khan – Fargo, ND, United States
Topic(s):  Applied Computing

Abstract

Recommendation systems were developed in the 90s to address the challenges of automatic and personalized selection of data from diverse and overloaded sources of information. These systems apply numerous knowledge discovery techniques on users’ historical and contextual data to suggest information, products, and services that best match the user’s preferences.

In this talk, we describe the fundamental concepts of recommendation systems, and how the techniques and methodologies are adapting in the realm of big data. As case studies, we will detail our work on developing recommendation systems for: (a) mobile social networks, (b) health insurance plans, (c) social venues, and (d) large-scale evacuations.  During the discussion on the cases studies, we also will touch on issues related to: (a) cold start, (b) data sparseness, and (c) scalability, which are the prime research concerns in big data recommendation systems.

About this Lecture

Number of Slides:  45
Duration:  60 minutes
Languages Available:  English
Last Updated: 

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