Improving Co-Cluster Quality with Application to Product Recommendations

Abstract

Businesses store an ever increasing amount of historical customer sales data. Given the availability of such information, it is advantageous to analyze past sales, both for revealing dominant buying patterns, and for providing more targeted recommendations to clients. In this context, co-clustering has proved to be an important data-modeling primitive for revealing latent connections between two sets of entities, such as customers and products.