Learning Recommendations from User Actions in the Item-poor Insurance Domain

TL;DR

This paper introduces a novel recurrent neural network model for session-based insurance recommendations, effectively addressing data scarcity by leveraging user session signals and outperforming existing methods.

Contribution

It presents the first session-based recommendation model tailored for the insurance domain, handling data sparsity and modeling cross-session dependencies.

Findings

Model outperforms state-of-the-art baselines on real-world insurance data.

Learning session dependencies significantly improves recommendation accuracy.

Dataset and model are made publicly available for research.

Abstract

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.