IEEE CIR: Building Real Time Recommendation Engines
We are seeing recommendation engines at work everywhere. When we switch on Netflix, the shows that we see on the screen are coming from a recommendation engine. When we search google, the results that come as well as the ads that are displayed are the result of a complex interplay of several recommendation engines. In this presentation we will look at the mathematics behind Recommendation Engines. The talk will be a presentation but attendees can follow the presenter by getting their hands dirty in building a simple recommendation engine that uses a collaborative filter. (The tutorial will show how to Build an actual recommendation engine that makes recommendations for product purchases and movie recommendations.) This presentation will describe how a graph database is useful when it comes to recommendations.
Date and Time
Location
Hosts
Registration
- Date: 18 Oct 2019
- Time: 06:00 PM to 08:00 PM
- All times are (GMT-07:00) US/Mountain
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- 2155 East Wesley Avenue
- Denver, Colorado
- United States 80208
- Building: Ritchie Engineering
- Room Number: 357
- Starts 21 July 2019 11:37 AM
- Ends 10 October 2019 06:37 PM
- All times are (GMT-07:00) US/Mountain
- No Admission Charge
Speakers
Ashwin Pingali
Building Real Time Recommendation Engines
We are seeing recommendation engines at work everywhere. When we switch on Netflix, the shows that we see on the screen are coming from a recommendation engine. When we search google, the results that come as well as the ads that are displayed are the result of a complex interplay of several recommendation engines. In this presentation we will look at the mathematics behind Recommendation Engines. The talk will be a presentation but attendees can follow the presenter by getting their hands dirty in building a simple recommendation engine that uses a collaborative filter. (The tutorial will show how to Build an actual recommendation engine that makes recommendations for product purchases and movie recommendations.)
Biography:
Ashwin Pingali has completed his PhD in Computer Science & Information Technology with the University of Colorado, Denver (All but Dissertation). An MBA in Operations, Information Systems and Marketing with the Indian Institute of Management and a BS in Engineering Technology (Gold Medal Honors) with the Jawaharlal Nehru Technological University, Hyderabad India. Ashwin is currently working as the CTO for Aivante a health care fintech startup and Apps Consultants a company focused on bringing Big Data technologies into everyday business operations. Dr (ABD) Pangali Dissertation research on “How IT can change mental models to improve decision-making effectiveness” has lead him to curtail his theoretical academia pursuits for more applied research path. Ashwin’s current research focus and skills are in Artificial Intelligence, Deep Learning and Cognitive Computing, and his specialty is in bringing both graph based mathematical representations to healthcare expenditure contexts, creating probabilistic models of reasoning and using Big Data predictive technologies to aid in the processing of large contextual graphs for actionable decision making.
Dr Panglai additionally consults with Dish Networks as a Data Scientist helping their budding Enterprise Data Science execute Predictive Modeling and Optimization projects in AdTech and Fraud Monitoring. Dr Pangali achievements at Dish Networks includes building a complex forecasting system to generate over 500,000 forecasts for improving yield management. Dr Pangali is currently working on Ad-Brain an initiative to embed AI into Ad capacity planning and pricing.