About Webinar: Build a Recommendation Engine for Retail Data
“A Webinar to introduce technique and in depth conceptual details to build a recommendation Engine”
Recommendation systems are one of the most common applications of big data and machine learning. There are many ways to build a recommendation system. Generally, it uses both the implicit and explicit data to build the Model but it has its own challenges like some of the rating information does not have upper bound and is continuous. For example , purchase data.
Silara Retail would like to build a recommendation system whose historical dataset does not have any explicit rating for the products purchased by the users.
In this session, we will help Silara Retail to build a recommendation system to predict the top 10 items for the new users.As a developer, we will understand the challenges with the dataset , need to create a new product Ranking based on the frequency of purchase, normalization to bring it to uniform scale and fitting it with Popularity model as baseline.
This session will be supported by the discussions around optimization technique and the demonstration of an implementation using IBM Cloud.
Why should you participate?
- Participants will learn the techniques and in-depth conceptual details , types of recommendation system like CF, CBF and Cosine Similarity , ALS
- Understand the approach between the dataset with closed ranking system and with the dataset which does not have upper bound
- Get an jumpstart baseline model for the online practise problem available through Analytics Vidhya
- Understand the IBM Cloud development environment to build a recommendation Engine
Rajesh K Jeyapaul
Architect , Developer Advocate & startup Mentor at IBM India
Rajesh K Jeyapaul is an Architect , Developer Advocate and startup Mentor @ IBM India. His technology focus is on AI and especially on Distributed environment.
In his 22 years of technology journey , he has led various product development projects on JVM technologies, OS , cloud OS and open source projects. Also, has led various enterprise solution enablement from database migration to hybrid cloud enablement for Banking and Telecom sector.
He started his career with Indian Defense Research organization as Research Fellow working in the cockpit simulation of India’s indigenous Light combat aircraft , Tejas.
He has co-authored RedBooks on PowerVM virtualizations & performances and having 4 patents to his credit.
Data Scientist at IBM
Binu Midhun is a Data Scientist and has overall 10 years of experience in IT Industry. She has worked on complex computational modules for Retail and Financial institutions. Completed Masters in Computer Science, is a Data enthusiast and experienced in Artificial Intelligence technologies.
IBM India Digital Business Group engages closely with Developer community by taking the latest in the technology through webinars, meetup and hands on workshop. Also, technology based solution document is created and made available as code patterns @ https://developer.ibm.com/patterns/
Developers are encouraged to be part of this journey. Details about the session is made available through the below city based meetup link:
More about IBM :https://www.ibm.com/in-en