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

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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.

Original languageEnglish
Title of host publicationRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems
Number of pages11
PublisherAssociation for Computing Machinery, Inc.
Publication date2022
Pages113-123
ISBN (Electronic)9781450392785
DOIs
Publication statusPublished - 2022
Event16th ACM Conference on Recommender Systems, RecSys 2022 - Seattle, United States
Duration: 18 Sep 202223 Sep 2022

Conference

Conference16th ACM Conference on Recommender Systems, RecSys 2022
Country/TerritoryUnited States
CitySeattle
Period18/09/202223/09/2022
SponsorACM Special Interest Group on Artificial Intelligence (SIGAI), ACM Special Interest Group on Computer-Human Interaction (SIGCHI), ACM Special Interest Group on Hypertext, Hypermedia, and Web (SIGWEB), ACM Special Interest Group on Information Retrieval (SIGIR), ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD)

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

  • Insurance Recommendation
  • Recurrent Neural Network
  • Session-based Recommender System

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