Optimizing Commercial Site Selection Using the Thompson Sampling Algorithm
DOI:
https://doi.org/10.62051/ztd60c92Keywords:
Thompson Sampling Algorithm; Commercial site selection; Multi-armed Bandit.Abstract
In the era of big data, businesses are inundated with vast amounts of information across a wide spectrum of types, creating both opportunities and challenges in data utilization. Among the various algorithms adapted for this environment, the Thompson Sampling algorithm stands out due to its exceptional capability to handle dynamic data and complex problems. This algorithm leverages probabilistic models to continuously update and optimize decision-making processes based on incoming data streams. Thompson Sampling excels particularly in determining the optimal locations for businesses by analyzing extensive databases integrated with sophisticated business location models. By assessing and adjusting to real-time data, it allows for a more nuanced understanding of geographical and demographic trends that are crucial for strategic business placement. This method not only enhances the accuracy of business decisions but also significantly improves adaptability in rapidly changing market conditions.
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