This system generate a personalized list of recommended products for each user.
- Begin by importing and visualizing the dataset. Organize the data into a 2D matrix format, utilizing User ID (UID) and Product ID as the basis for recording user interactions.
- Computed the features for each user. In this context, features represent the count of distinct products purchased by each user.
- Employ a nearest neighbor algorithm to identify the five closest neighbors for each user. These neighbors will be chosen based on similarity in product purchase behavior.
- Combine the products purchased by the current user and their five nearest neighbors (including the user itself). This forms a union of products collectively bought by this group.
- Implemented the process of systematically identifying nearest neighbors and aggregating purchased products for each user. This will result in the creation of a recommendation database tailored to individual users.