Product Recommendation System for E-Commerce

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WeWork

1161 Mission St · San Francisco, CA

How to find us

The nearest BART/MUNI stop is Civic Center. Meetup will be held in the First Floor Lounge.

Location image of event venue

Details

Speaker: Amir Meimand, Director of Science Research and Development at Zilliant
https://www.linkedin.com/in/amirmeimand/

Topic:
Product Recommendation System for E-Commerce

Schedule:
6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments
6:30pm - 7:20pm - Talk
7:20pm - 7:30pm - Q&A
7:30pm - 8:00pm - Networking

IMPORTANT: Please bring your photo ID and sign in at the front desk.

Bio:
Amir Meimand is Zilliant Director of Science Research and Development, pricing scientist, where he designs and develops pricing solutions for customers and performs research in which he applies new methods to improve the current solutions as well as develop new tools. His primary role is to oversee the company's R&D projects to ensure that company meet its objectives for researching and developing new products or technologies and improve existing products.

Abstract:
In e-commerce world recommendation systems play a key role in elevating customer experience by reduce the number of clicks to find the items they want to purchase. In B2B e-commerce in addition to customer satisfaction another important aspect of recommendation system from business perspective is to increase the average order size over time. An effective recommendation system specially for B2B business should not only recommend items which were frequently purchased in the past by a customer but also should be able to identify the items which were never purchased in past but are likely to be in interest of the customers.

In this presentation we introduce a novel 2 steps method to design a recommendation system which can meet both goals. In the first step we employed association rules mining to compute the similarity between each pair of customers and in the second step a clustering method to identify the group of customers which are likely have similar purchasing behavior. We also present some analytical approach on how set and tune the hyper parameters of the clustering technique to get accurate result.

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