Recommendation algorithms form an integral aspect of the modern streaming service, with most major services utilising one. However, one area where these services typically lack is explainability, with recommendations usually given with no to little explanation, making the process ambiguous to the user. There is also a lack of control within these systems, with the user unable to define what they would like to see. To address these issues, this project creates a music album recommendation website that users can feed their music opinions and control what they’re recommended. Personalised recommendations are then generated based on these views and subsequently justified by displaying why the user could like or relate to the recommended music. The system implements a collaborative filtering approach utilising alternating least squares (ALS) to create personalised recommendations. To support the collaborative filtering approach user surveys were utilised to acquire data. The findings of the report show that users display positive sentiment towards recommendation explanations and a greater freedom of control on what is recommended. This report will discuss the research, design process and implementation of this application.