Publicação
Intelligent recommender system for car insurance plans - focusing on model flexibility and personalisation
| Resumo: | Acknowledging the success of personalized recommendations as support to promote sales within a business, this paper proposes the development of a recommender system to answer Fidelidade’s problem of depersonalization in the auto insurance sector. To build a model able to consider historical data from the customer and the car to recommend the best auto insurance package, a thorough data cleaning and model hypertunning were made to ensure that the three main objectives: predictability (accuracy when predicting), explainability (explaining to each customer the reason to recommend a certain product) and flexibility (proposing different coverages combinations) were satisfied. |
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| Autores principais: | Pótsa, Tamás |
| Assunto: | Machine learning Content-based recommendation systems Insurance Predictability Explainability Flexibility |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Acknowledging the success of personalized recommendations as support to promote sales within a business, this paper proposes the development of a recommender system to answer Fidelidade’s problem of depersonalization in the auto insurance sector. To build a model able to consider historical data from the customer and the car to recommend the best auto insurance package, a thorough data cleaning and model hypertunning were made to ensure that the three main objectives: predictability (accuracy when predicting), explainability (explaining to each customer the reason to recommend a certain product) and flexibility (proposing different coverages combinations) were satisfied. |
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