Learning dynamic insurance recommendations from users' click sessions
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
While personalised recommendations have been most successful in domains like retail due to large volume of users' feedback on items, it is challenging to implement traditional recommender systems into the insurance domain where such prior information is very small in volume. This work addresses the problem of sparse feedback by studying users' click sessions as signals for learning insurance recommendations. Our preliminary results show limitations in representing click sessions by manually engineered features. The proposed framework uses an autoencoder approach to automatically learns representation of sessions, then a neural network approach to model dependencies across sessions that can be used to predict recommendations. Thereby, it is further able to capture users' dynamic needs of insurance products evolving over time.
Original language | English |
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Title of host publication | RecSys 2021 - 15th ACM Conference on Recommender Systems |
Number of pages | 4 |
Publisher | Association for Computing Machinery, Inc. |
Publication date | 13 Sep 2021 |
Pages | 860-863 |
ISBN (Electronic) | 9781450384582 |
DOIs | |
Publication status | Published - 13 Sep 2021 |
Event | 15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online, Netherlands Duration: 27 Sep 2021 → 1 Oct 2021 |
Conference
Conference | 15th ACM Conference on Recommender Systems, RecSys 2021 |
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Land | Netherlands |
By | Virtual, Online |
Periode | 27/09/2021 → 01/10/2021 |
Sponsor | ACM 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 (ACM Special Interest Group on Hypertext, Hypermedia, and Web), ACM Special Interest Group on Information Retrieval (SIGIR), ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD), Special Interest Group on Economics and Computation (SIGecom) |
Series | RecSys 2021 - 15th ACM Conference on Recommender Systems |
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Bibliographical note
Publisher Copyright:
© 2021 Owner/Author.
- Click-based models, Insurance domain, Session-based recommender systems
Research areas
ID: 306689949