Abstract
The black box nature of the recommendation systems limits the understanding and acceptance of the recommendation received by the user. In contrast, user interaction and information visualization play a key role in addressing these drawbacks. In the brokerage domain, insurance brokers offer, negotiate and sell insurance products for their customers. Support brokers into the recommendation process can improve the loyalty, profit, and marketing campaign in their client portfolio. This work presents Broker-Insights, an interactive and visualisation-based insurance products recommender system to support brokers into the decision-making (recommendation) at two levels: recommendations for a specific potential customer; and recommendations for a group of customers. Looking for offering personalized recommendations, Broker-Insights provides a tool to manage customers information in the recommendation task and a module to perform customers segmentation based on specific characteristics. With the help of an eye-tracker, we evaluated Broker-Insigths usability with ten naive users on the offline fashion and also performed an evaluation in the wild with three insurance brokers. Results achieved show that data mining methods, while combined with interactive data visualization improved the user experience and decision-making process into the recommendation task, and increased the products recommendation acceptance.
This study was partly funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001, and by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).
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Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 6, 734–749 (2005)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22, 207–216 (1993)
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Valdez, A.C., Ziefle, M., Verbert, K.: HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 123–126. ACM (2016)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 5, 603–619 (2002)
Gupta, A., Jain, A.: Life insurance recommender system based on association rule mining and dual clustering method for solving cold-start problem. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), 1356–1360 (2013)
Hahsler, M., Chelluboina, S.: Visualizing association rules: introduction to the r-extension package arulesViz. R Project Module, pp. 223–238 (2011)
Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87(3), 317–335 (2017)
He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)
Jugovac, M., Jannach, D.: Interacting with recommenders-overview and research directions. ACM Trans. Interact. Intell. Syst. (TiiS) 7(3), 10 (2017)
Karimi, M., Jannach, D., Jugovac, M.: News recommender systems-survey and roads ahead. Inf. Process. Manag. 54(6), 1203–1227 (2018)
Lewis, C., Rieman, J.: Task-centered user interface design. A practical introduction (1993)
Lewis, J.R.: The system usability scale: past, present, and future. Int. J. Hum.-Comput. Interact. 34(7), 577–590 (2018). Taylor & Francis
Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4), 2065–2073 (2014)
Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)
Mitra, B.P.S., Chaudhari, N., Patwardhan, B.: Leveraging hybrid recommendation system in insurance domain. Int. J. Eng. Comput. Sci. 3(10), 8988–8992 (2014)
Mukherji, A., et al.: FIRE: a two-level interactive visualization for deep exploration of association rules. Int. J. Data Sci. Anal. 7(3), 201–226 (2019)
Pandey, A.K., Rajpoot, D.S.: Resolving cold start problem in recommendation system using demographic approach. In: 2016 International Conference on Signal Processing and Communication (ICSC), pp. 213–218. IEEE (2016)
Qazi, M., Fung, G.M., Meissner, K.J., Fontes, E.R.: An insurance recommendation system using Bayesian networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 274–278. ACM (2017)
Rahman, S.S.A., Norman, A.A., Soon, K.: MyINS: a CBR e-commerce application for insurance policies. Electron. Commer. Res. 5(1), 373–380 (2006)
Rokach, L., Shani, G., Shapira, B., Chapnik, E., Siboni, G.: Recommending insurance riders. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 253–260. ACM (2013)
Sobhanam, H., Mariappan, A.: Addressing cold start problem in recommender systems using association rules and clustering technique. In: 2013 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–5. IEEE (2013)
Solanki, S.K., Patel, J.T.: A survey on association rule mining. In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ACCT), pp. 212–216. IEEE (2015)
Xu, W., Wang, J., Zhao, Z., Sun, C., Ma, J.: A novel intelligence recommendation model for insurance products with consumer segmentation. J. Syst. Sci. Inf. 2(1), 16–28 (2014)
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Atauchi, P.D., Nedel, L., Galante, R. (2019). Broker-Insights: An Interactive and Visual Recommendation System for Insurance Brokerage. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_13
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