A Model-Based Recommendation Systems for B2B e-Commerce by Amir Meimand

Full title of the talk - A Model-Based Recommendation Systems for B2B e-Commerce with Clustering and Association Rules Mining.

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 prestation 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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