Toward interpretable predictive models in B2B recommender systems

Recommender systems are becoming increasingly important for businesses because they can help companies offer personalized product recommendations to customers. There have been many acknowledged recognized successes of consumer-oriented recommender systems, particularly in e-commerce. In this work, we describe our experiences building a business-to-business (B2B) recommendation engine that matches company clients to internal company products. The underlying pairing of clients and products is based on co-clustering principles and helps reveal potential future purchases. Unlike most consumer-oriented recommendation systems, our approach takes into account the need for interpretability. We do not only provide a cursory explanation, as offered in most traditional recommender systems. In our approach, the recipient of the generated recommendations are sales and marketing teams; thus, we offer a detailed reasoning in straightforward English that considers multiple aspects regarding why a client may be a suitable match for the particular offering. Finally, we analyze the outcome of a country-wide deployment of the proposed methodology for selected IBM sales teams.