Data warehouse and mining semester project
Once the ETL pipeline is set up, the recommendation system can use the transformed and loaded data to generate personalized music recommendations for each user. The motivation for a music recommendation system based on ETL is to provide personalized music recommendations to users based on their listening history, preferences, and activity. We will use an ETL pipeline to extract data from various sources, transform it into a form that can be used by the recommendation system, and load it into a data storage system for fast querying. The recommendation system should be able to handle structured and unstructured data and should be able to perform data transformations and enrichment as needed.
In conclusion, a music recommendation system based on ETL and ML can be a powerful and flexible solution for generating personalized music recommendations for users. It may also be helpful to compare the performance of the recommendation system with a baseline or a control group, such as a group of users who do not receive recommendations or a group of users who receive recommendations from a different system.
