Speaker: Amir Meimand, Director of Science Research & Development at Zilliant
Product Recommendation System for E-Commerce
6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments
6:30pm - 7:20pm - Talk
7:20pm - 7:30pm - Q&A
7:30pm - 8:00pm - Networking
Amir Meimand is Zilliant Director of Science Research & Development, pricing scientist, where he designs & develops pricing solutions for customers & 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 & developing new products or technologies & improve existing products.
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 & 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 & tune the hyper parameters of the clustering technique to get accurate result.
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