Learning Dynamic Insurance Recommendations from Users’ Click Sessions

Bruun, Simone Borg

doi:10.1145/3460231.3473900

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2023

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, large‐scale research has scarcely been conducted due to the unavailability of data to the public, questioning the data quantity of prior studies. Borg Bruun (2021) also highlighted these issues and attempted to solve the quality issues of insurance data by adding user click sessions of insurance products. Furthermore, Rokach et al (2013) and Qazi et al (2020) indicated that insurance products have unique properties that differentiate them from classic recommended products.…”

Section: Introductionmentioning

confidence: 99%

Constructing a personalized recommender system for life insurance products with machine‐learning techniques

Kong

Yun

Joo

et al. 2022

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

The collaborative filtering (CF) recommendation algorithm predicts the purchases of specific users based on their characteristics and purchase history. This study empirically analyzes the possibility of applying CF to the insurance industry using real customer data from South Korea. Using three different CF models, we examined the relevance of applying the CF model to insurance products under various situations by comparing them with logistic-regression-based recommendation models. Through experiments, we empirically show that CF models apply to the insurance industry, especially when customer purchase information is added to the model.

show abstract

Section: Introductionmentioning

confidence: 99%