<![CDATA[When shopping at an online store, customers are always confused by the number of products displayed in the catalog. As a solution, customers see testimonials and other user ratings to ensure that the product to be purchased is in accordance with what is displayed in the product catalog. User-base collaborative filtering is a recommendation method that uses rating data from users to generate recommendations. To create a recommendation system using this method, there are three steps that must be taken, namely, first calculating the rating distance between users using the Euclidean distance equation, second calculating the similarity between active users and other users based on Euclidean distance calculations, and third calculating predictions. rating, which is to transfer the similarity value to the user rating value and then add up all the multiplication values. The final result of the prediction calculation is the five highest recommendation values which will then be processed by the system in such a way as to produce recommendations for shoes products that are displayed to active users as product recommendations. With this recommendation system, it is hoped that it will further increase consumer convenience when shopping at online stores]]>
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