A Business Recommender System Based on Zones and Commercial Data

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Dameh, Rania
Egbaria, Ruba
Alzaqa, Salsabeel
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In many countries people target different places to open a business and succeed in it. They may choose an unsuccessful business or the location does not need the type of this business. In this paper, we aim to improve the opportunity of choosing a correct business and location. We suggest an approach based on many principles of machine learning. The approach uses a prediction model based on analysing data about zones (areas) and their commercial services. The zones are classified using K-Means clustering method that depends on the number of same businesses and their costs averages in an area. To show the novelty of our work, we developed a system that implements the approach principles for several zones in Nablus city. We evaluate the work by running several test cases to show the system ability in recommending kinds of businesses.