short-paper

An Insurance Recommendation System Using Bayesian Networks

Authors:

Katie J. Meissner,

Eduardo R. FontesAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 274 - 278

https://doi.org/10.1145/3109859.3109907

Published: 27 August 2017 Publication History

Abstract

In this paper we describe a deployed recommender system to predict insurance products for new and existing customers. Our goal is to give our customers personalized recommendations based on what other similar people with similar portfolios have, in order to make sure they were adequately covered for their needs. Our system uses customer characteristics in addition to customer portfolio data. Since the number of possible recommendable products is relatively small, compared to other recommender domains, and missing data is relatively frequent, we chose to use Bayesian Networks for modeling our system. Experimental results show advantages of using probabilistic graphical models over the widely used low-rank matrix factorization model for the insurance domain.

References

[1]

Bayesialab. http://www.bayesia.com/. Versions: 5.4.3 & 6.0.2; Accessed: 1-Feb-2017.

[2]

IMC software. http://bigdata.ices.utexas.edu/software/inductive-matrix-completion/. Online; Accessed 1-Feb-2017.

[3]

Netica application. https://www.norsys.com/netica.html. Accessed: 1-Feb-2017.

[4]

J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, UAI'98, pages 43--52, San Francisco, CA, USA, 1998. Morgan Kaufmann Publishers Inc.

Digital Library

[5]

H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah. Wide and deep learning for recommender systems. arXiv:1606.07792, 2016.

[6]

T. Fawcett. An introduction to roc analysis. Pattern Recogn. Lett., 27(8):861--874, June 2006.

Digital Library

[7]

A. Felfernig and A. Kiener. Knowledge-based interactive selling of financial services with fsadvisor. In Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence - Volume 3, IAAI'05, pages 1475--1482. AAAI Press, 2005.

Digital Library

[8]

A. Gupta and A. Jain. Life insurance recommender system based on association rule mining and dual clustering method for solving cold-start problem. International Journal of Advanced Research in Computer Science and Software Engineering, 3, Oct 2013.

[9]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10--18, Nov. 2009.

Digital Library

[10]

T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell., 24(7):881--892, July 2002.

Digital Library

[11]

K. R. Karkera. Building Probabilistic Graphical Models with Python. Packt Publishing, 2014.

Digital Library

[12]

D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, 2006.

Digital Library

[13]

K. P. Murphy. An introduction to graphical models. Technical report, 2001.

[14]

R. Nagarajan and M. Scutari. Bayesian Networks in R with Applications in Systems Biology. Springer, New York, 2013. ISBN 978-1-4614-6445-7, 978-1-4614-6446-4.

Digital Library

[15]

N. Natarajan and I. S. Dhillon. Inductive matrix completion for predicting gene-disease associations. Bioinformatics, 30, jun 2014.

[16]

L. Rokach, G. Shani, B. Shapira, E. Chapnik, and G. Siboni. Recommending insurance riders.

[17]

B. P. Sanghamitra Mitra, Nilendra Chaudhari. Leveraging hybrid recommendation system in insurance domain. International Journal of Engineering and Computer Science, 3, Oct 2014.

[18]

M. Scutari. Learning bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3):1--22, 2010.

[19]

M. Scutari. Bayesian network constraint-based structure learning algorithms: Parallel and optimized implementations in the bnlearn R package. Journal of Statistical Software, 77(2):1--20, 2017.

[20]

M. Scutari and J.-B. Denis. Bayesian Networks with Examples in R. Chapman and Hall, Boca Raton, 2014. ISBN 978-1-4822-2558-7, 978-1-4822-2560-0.

[21]

D. Shin, S. Cetintas, K.-C. Lee, and I. S. Dhillon. Tumblr blog recommendation with boosted inductive matrix completion. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, pages 203--212, New York, NY, USA, 2015. ACM.

Digital Library

[22]

F. Strub, R. Gaudel, and J. Mary. Hybrid Recommender System based on Autoencoders. In the 1st Workshop on Deep Learning for Recommender Systems, pages 11--16, Boston, United States, Sept. 2016.

Digital Library

[23]

H.-F. Yu, P. Jain, P. Kar, and I. S. Dhillon. Large-scale multi-label learning with missing labels. In Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32, ICML'14, pages I-593--I-601. JMLR.org, 2014.

Digital Library

Cited By

Borg Bruun SBalog KMaistro MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Dataset and Models for Item Recommendation Using Multi-Modal User InteractionsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657881(709-718)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657881
Bruun SLioma CMaistro M(2024)Recommending Target Actions Outside Sessions in the Data-poor Insurance DomainACM Transactions on Recommender Systems10.1145/36069503:1(1-24)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3606950
Ekanayake SZois D(2024)Datum-Wise Inference in Structured EnvironmentsIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32716165:2(566-577)Online publication date: Feb-2024
https://doi.org/10.1109/TAI.2023.3271616
Show More Cited By

Recommendations

Neighborhood Constraints Based Bayesian Personalized Ranking for Explainable Recommendation
Web and Big Data
Abstract
Model-based Collaborative Filtering (CF) methods such as Matrix Factorization (MF) have achieved promising ranking performance in recent years. However, these methods tend to be a black box that cant not provide any explanation for users. To ...
New Recommendation Techniques for Multicriteria Rating Systems

Traditional single-rating recommender systems have been successful in a number of personalization applications, but the research area of multicriteria recommender systems has been largely untouched. Taking full advantage of multicriteria ratings in ...
A framework for diversifying recommendation lists by user interest expansion

Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

RecSys '17

Sponsor:

SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
1,405
Total Downloads

Downloads (Last 12 months)71
Downloads (Last 6 weeks)3

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Borg Bruun SBalog KMaistro MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Dataset and Models for Item Recommendation Using Multi-Modal User InteractionsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657881(709-718)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657881
Bruun SLioma CMaistro M(2024)Recommending Target Actions Outside Sessions in the Data-poor Insurance DomainACM Transactions on Recommender Systems10.1145/36069503:1(1-24)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3606950
Ekanayake SZois D(2024)Datum-Wise Inference in Structured EnvironmentsIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32716165:2(566-577)Online publication date: Feb-2024
https://doi.org/10.1109/TAI.2023.3271616
Liu HWong R(2023)Automatic Personalized Health Insurance Recommendation Based on Utility and User Feedback2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00174(1360-1369)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00174
Hosein P(2023)A data science approach to risk assessment for automobile insurance policiesInternational Journal of Data Science and Analytics10.1007/s41060-023-00392-x17:1(127-138)Online publication date: 22-Mar-2023
https://doi.org/10.1007/s41060-023-00392-x
Zhang J(2022)Design and Implementation of Insurance Product Recommendation SystemFrontiers in Computing and Intelligent Systems10.54097/fcis.v1i2.17741:2(63-66)Online publication date: 28-Sep-2022
https://doi.org/10.54097/fcis.v1i2.1774
Borg Bruun SMaistro MLioma C(2022)Learning Recommendations from User Actions in the Item-poor Insurance DomainProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546775(113-123)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546775
Sidorowicz TPeres PLi Y(2022)A Novel Approach for Cross-Selling Insurance Products Using Positive Unlabelled Learning2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892762(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892762
Hosein P(2022)A Data-Driven Pricing Strategy for Automobile Insurance Policies2022 5th Asia Conference on Machine Learning and Computing (ACMLC)10.1109/ACMLC58173.2022.00009(1-4)Online publication date: Dec-2022
https://doi.org/10.1109/ACMLC58173.2022.00009
Albo C(2022)Alternative Data for Configurable and Personalized Commercial Insurance ProductsBig Data and Artificial Intelligence in Digital Finance10.1007/978-3-030-94590-9_18(313-322)Online publication date: 29-Apr-2022
https://doi.org/10.1007/978-3-030-94590-9_18
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents