Abstract
Coupon marketing is one of the oldest and most effective business marketing methods, in which special offers on products or services entice clients. This paper proposes a framework that acts as a coupon recommender system for in-vehicle users collaborating Machine Learning techniques to suggest the most suitable coupon for the user based on their attributes. ANN, CNN, LSTM, Random Forest, XGBoost, and Stacking Machine Learning Algorithms are applied to train and test the dataset to determine the most efficient and accurate algorithm that can predict the desirable coupon for the vehicle user. Accuracy, Precision, Recall, and F1-scores were calculated and compared of these algorithm models to obtain the most efficient model for the framework. Lastly, the framework determines the possibilities of accepting the available coupons based on the user features using the best-performing algorithm and recommends the coupon, thus providing a highly beneficial and effective system for the in-vehicle users and business owners.
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Hai, M.A., Shartaj Uddin, R., Rahman, Y., Mahfuza, R. (2022). A Methodology for Recommending In-Vehicle Coupons Incorporating Machine Learning Algorithms for Efficient Financial Schemes. In: Hossain, S., Hossain, M.S., Kaiser, M.S., Majumder, S.P., Ray, K. (eds) Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021 . Lecture Notes in Networks and Systems, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-19-2445-3_2
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DOI: https://doi.org/10.1007/978-981-19-2445-3_2
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