Amazon Recommender System.

The primary objective of the Amazon Recommender System database management project was to evaluate customers’ purchasing behaviors on Amazon and recommend related products to increase profits. The project aimed to solve numerous business issues, such as identifying the top paying customers in December, determining the most popular payment method, and identifying the sub-category with the highest sales during the same month.

Moreover, the project aimed to develop a recommender system based on collaborative filtering and purchase data to suggest the most relevant products to a specific customer based on their previous orders. A dataset containing customer information, order information, and payment information was employed for the project. Seven tables, including customers, orders, payments, main-categories, sub-categories, recommender system, and order-subcategory, were created to model the dataset.

The tables were linked using primary keys, and entity relationship diagrams were used to visualize the database structure. The project encountered issues such as authorization limitations and the need to modify data models multiple times during the query process due to unfamiliarity with the recommender system and its functions. Despite these challenges, the project provided opportunities to perform several database management tasks, including CREATE/DROP table, INSERT values, and use SELECT statements to analyze data.

The project laid the groundwork for future recommender system development, and the knowledge gained from it can be used to improve future projects and professional experience. Overall, the project highlights the importance of effective recommender systems in today’s digital world and the potential of data-driven insights for businesses.