Abstract
Abstract: In this paper we describe a deployed recommender system to predict insurance products for new and existing beneficiary. Our main objective is to provide our customers individual suggestions depend upon what other similar people have same portfolios, in order to make sure they were adequately covered for their needs. Our system uses customer behavior in addition to customer portfolio data. Since the number of probable products is relatively small, as compared to other suggested domains, and missing data is relatively frequent, so we decide to use Bayesian Networks for designing our system. Experimental results show advantages of using probabilistic graphical models over the widely used low-rank matrix factorization model for the insurance domain. Keywords: Recommender systems; Bayesian Networks; Insurance domain; Structure Learning; Deployed system
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