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. Our goal is to give our customers personalized recommendations based on what other similar people with similar portfolios have, in order to make sure they are adequately covered for their needs. Our system uses customer characteristics in addition to customer portfolio data. Since the number of possible recommendable products is relatively small, compared to other recommender domains, and missing data is relatively frequent, we chose to use Bayesian Networks for modeling our systems. We also present a deep‐learning‐based approach to provide recommendations to prospects (potential customers) where only external marketing data is available at the time of prediction.

中文翻译：

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使用机器学习设计和部署保险推荐系统

推荐系统对于各种类型的行业都变得极为重要，在这些行业中，客户的互动和反馈对于企业的成功至关重要。对于面对不断增长的市场所带来的变化的公司而言，向新老客户提供产品推荐是一项挑战。我们的目标是根据具有相似投资组合的其他相似人员的意见，为我们的客户提供个性化建议，以确保他们能够充分满足他们的需求。除了客户投资组合数据之外，我们的系统还使用客户特征。与其他推荐域相比，由于可能的推荐产品数量相对较少，并且数据丢失相对频繁，因此我们选择使用贝叶斯网络对系统进行建模。