A Recommender System for Insurance Packages Based on Item-Attribute-Value Prediction

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dc.contributor.authorChawuthai, Rathachai
dc.contributor.authorChoosak, Chananya
dc.contributor.authorWeerayuthwattana, Chutika
dc.date.accessioned2026-08-06T10:31:24Z
dc.date.available2026-08-06T10:31:24Z
dc.date.issued2021-01-01
dc.description.abstractFinding a proper insurance package becomes a challenging issue for new customers due to the variety of insurance packages and many factors from both insurance packages’ policies and users’ profiles for considering. This paper introduces a recommender model named INSUREX that attempts to analyze historical data of application forms and contact documents. Then, machine learning techniques based on item-attribute-value prediction are adopted to find out the pattern between attributes of insurance packages. Next, our recommender model suggests several relevant packages to users. The measurement of the model results in high performance in terms of HR@K and F1-score. In addition, a web-based proof-of-concept application has been developed by utilizing the INSUREX model in order to recommend insurance packages and riders based on a profile from the user input. The evaluation against users demonstrates that the recommender model helps users get start in choosing right insurance plans.
dc.identifier.citationCommunications in Computer and Information Science, 1371 CCIS, 73-84, 2021
dc.identifier.doi10.1007/978-981-16-1685-3_7
dc.identifier.issn18650929
dc.identifier.other2-s2.0-85104791436
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11713
dc.sourceCommunications in Computer and Information Science
dc.subjectAttribute-value prediction
dc.subjectData analytics
dc.subjectInsurance package
dc.subjectMachine learning
dc.subjectRecommender system
dc.titleA Recommender System for Insurance Packages Based on Item-Attribute-Value Prediction
dc.typeConference Paper

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