  While personalised recommendations are successful in domains like retail,
where large volumes of user feedback on items are available, the generation of
automatic recommendations in data-sparse domains, like insurance purchasing, is
an open problem. The insurance domain is notoriously data-sparse because the
number of products is typically low (compared to retail) and they are usually
purchased to last for a long time. Also, many users still prefer the telephone
over the web for purchasing products, reducing the amount of web-logged user
interactions. To address this, we present a recurrent neural network
recommendation model that uses past user sessions as signals for learning
recommendations. Learning from past user sessions allows dealing with the data
scarcity of the insurance domain. Specifically, our model learns from several
types of user actions that are not always associated with items, and unlike all
prior session-based recommendation models, it models relationships between
input sessions and a target action (purchasing insurance) that does not take
place within the input sessions. Evaluation on a real-world dataset from the
insurance domain (ca. 44K users, 16 items, 54K purchases, and 117K sessions)
against several state-of-the-art baselines shows that our model outperforms the
baselines notably. Ablation analysis shows that this is mainly due to the
learning of dependencies across sessions in our model. We contribute the first
ever session-based model for insurance recommendation, and make available our
dataset to the research community.
