  Providing personalized recommendations for insurance products is particularly
challenging due to the intrinsic and distinctive features of the insurance
domain. First, unlike more traditional domains like retail, movie etc., a large
amount of user feedback is not available and the item catalog is smaller.
Second, due to the higher complexity of products, the majority of users still
prefer to complete their purchases over the phone instead of online. We present
different recommender models to address such data scarcity in the insurance
domain. We use recurrent neural networks with 3 different types of loss
functions and architectures (cross-entropy, censored Weibull, attention). Our
models cope with data scarcity by learning from multiple sessions and different
types of user actions. Moreover, differently from previous session-based
models, our models learn to predict a target action that does not happen within
the session. Our models outperform state-of-the-art baselines on a real-world
insurance dataset, with ca. 44K users, 16 items, 54K purchases and 117K
sessions. Moreover, combining our models with demographic data boosts the
performance. Analysis shows that considering multiple sessions and several
types of actions are both beneficial for the models, and that our models are
not unfair with respect to age, gender and income.
