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Recurrent Neural Network (RNN) is a type of artificial neural network widely used in retail applications. RNNs are designed to process sequential data by incorporating feedback connections, allowing them to capture temporal dependencies. In the retail context, RNNs can be leveraged for various tasks. For instance, they can analyze historical sales data to forecast future demand, enabling better inventory management and supply chain optimization.

RNNs can also be employed in customer behavior modeling, predicting purchasing patterns, and recommending personalized product suggestions. By processing sequential data over time, RNNs excel in capturing and understanding complex patterns within retail datasets. Their ability to handle sequential data makes RNNs a valuable tool in retail analytics and decision-making, enhancing operational efficiency and driving business growth.