Abstract
Consumer behavior is one of the most important issues that has been discussed in recent decades. Organizations always want to understand how consumer make decisions so that they can use it to design their products and services. Having a correct understanding of the consumers and the consumption process has many advantages. These advantages include helping managers make decisions, providing a cognitive basis through consumer analysis, helping legislators and regulators legislate on the purchase and sale of goods and services, and ultimately helping consumers make better decisions. Here is a solution for recommending goods based on the users’ past behavior over deep learning. The architecture expressed for deep learning is trained by users’ past behavioral data. Amazon data was studied and the results indicated that the proposed method has a much higher accuracy than similar methods.
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Mahdavi, R., Hasanjani Roshan, A. (2019). Smart Recommendation System Based on Understanding User Behavior with Deep Learning. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_5
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DOI: https://doi.org/10.1007/978-3-030-33495-6_5
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