Using Discriminative Graphical Models for Insurance Recommender Systems

Recommender systems have become extremely important to various types of industries where customer interaction and feedback is paramount to the success of the business. For companies that face changes that arise with ever-growing markets, providing product recommendations to new and existing customers is a challenge. Furthermore, it is important to have an algorithm which is descriptive, scalable, agnostic to missing features, and robust in providing these recommendations. Directed graphical models meet all these demands; however, if the dimensionality of the features is high, structure learning and inference can become computationally prohibitive. In this work, we propose an algorithm with some novel aspects to learn the structure of a graphical model (e.g. Bayesian network), which considerably speeds up both training (from days to minutes in some cases) and inference run-times with respect to standard Bayesian structure learning approaches, while achieving similar accuracy. We also show that this approach produces more accurate predictions than a state-of-the-art matrix factorization algorithm in the absence of complete evidence on several insurance-related datasets.