Speaker "Amir Meimand" Details Back



An Efficient ML-Based Algorithm for Mining Substitute Products for E-Commerce


In design the recommendations system for retailers and suppliers it is very important to understand the relationship between products based on customer behavior. In general, if two products are related they can be either substituted or complementary. Extracting both complimentary and substitute products provides valuable knowledge for market prediction. Complementary rules mining by discovering the association rules and it is goal is to identify the products are purchased together.

There are considerable amount of efforts in the literature to improve the efficient and effectiveness of association rules mining. Substitute rules mining are mostly based on negative association rules mining. There are two main difficulties in recognizing substitute products using negative association rules. Firstly, mining negative association rules are computationally very expensive. Moreover, negative association rules usually generate a lot of redundant rules and needs.

In this talk we introduce an innovative approach to discover substitute product by deriving the similarities of products based on corresponding association rules. The proposed method is computationally efficient and effective. We will present a case study to demonstrate the application of proposed method in B2B e-commerce business


Amir Meimand is Zilliant Director of R&D, 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. Prior to joining Zilliant, Amir helped design and develop a promotion planning and pricing platform for B2C retailers. Amir holds a dual Ph.D. degree in Industrial Engineering and Operations Research from Pennsylvania State University. In his doctoral work, he applied operations research concepts to dynamic pricing and revenue management.