Aura Music: A Scalable Personalized Music Streaming System Using Time-Weighted Recommendation and Hybrid Filtering

Abstract

The recent explosion in the online music consump- tion has resulted in a requirement of smart systems that are able to deliver users highly personalized content. The classic methods of recommendations tend to utilize fixed popularity values or a small amount of user (listener) preferences, which do not match the dynamic listening behavior. Paper presents scalable and intelligent streaming of music using Aura Music as an example, which is a hybridized recommendation framework based on collaborative filtering, behavioral analytics, and time-weighted scoring. It is developed on Android MVVM architecture-based client that will guarantee modularity and lifecycle-conscious data management, and the backend is built on Flask ensuring that it offers RESTful user request processing. Firebase Firestore constantly logs and processes user interactions (song plays, likes and the history of their listening) in real time. Time-weighted scoring system favours the recent user activity which provides context-sensitive recommendations. There is also the inclusion of diversity filtering layer to avoid redundant suggestions and further user exploration. Listening habits like artist distribution, time of day usage patterns and most popular songs are also incorporated into the system and give analytical feedback to the users. Caching strategies and effective API communication attain performance optimization. There is better recommendation accuracy, latency, and user engagement as shown by experimental evaluation. The proposed system emphasizes the usefulness of using a combination of temporal dynamics and hybrid filtering in the contemporary music recommendation system.

Introduction

The text discusses the evolution of music recommendation systems and introduces Aura Music, a smart streaming platform designed to improve personalization and user experience.

Traditional recommendation methods like popularity-based filtering are not effective in capturing users’ changing preferences. Common approaches such as collaborative filtering and content-based filtering help personalize recommendations but suffer from issues like cold-start problems, data sparsity, and over-specialization. To overcome these limitations, hybrid recommendation systems are used, combining multiple techniques for better accuracy. However, many existing systems still fail to adapt dynamically to real-time user behavior.

The proposed Aura Music system addresses these issues by integrating collaborative filtering, time-aware scoring, and diversity-based recommendations. It uses user interactions such as plays and likes, stored in Firebase Firestore, to continuously update recommendations through a feedback loop. The system is built using an Android MVVM architecture with a Flask backend, ensuring scalability, modularity, and real-time performance.

Key contributions include improved personalization using hybrid algorithms, behavioral analytics for tracking user preferences, diversity-aware recommendations to avoid repetitive content, and a scalable cloud-based infrastructure using Firebase and RESTful APIs. The system also continuously adapts to changing user interests.

The literature review explains major recommendation approaches: collaborative filtering, content-based filtering, hybrid models, temporal dynamics, diversity-aware methods, and scalable architectures. Each improves recommendations in different ways but also has limitations such as sparsity, over-specialization, or computational complexity.

The research gap identified is the lack of a unified system that combines accuracy, diversity, temporal awareness, and scalability in one framework. Aura Music aims to fill this gap by integrating all these techniques into a single adaptive, cloud-based recommendation system that provides more accurate and engaging music suggestions.

Conclusion

This paper also introduced Aura Music, a scalable and smart music streaming system that combines hybrid recommendation methods with the current mobile and cloud-architecture. The system integrates collaboration filtering, content-based filter- ing, time-weighted scoring, and diversity-sensitive systems to produce customized and context-sensitive music suggestions. The system implementation on the basis of an Android application (MVVM) and a Flask-based Backend and Firebase Firestore could provide an effective data processing, real-time synchronization, and user-friendly interaction. The outcomes of the experiments indicate that the system is able to involve itself in the habit of the user by indicating correct and varied suggestions to the user as a result of continuous feedback. Other ancillary functions like mood-based recommendation, listening insights and audio equalization, however, add to the user engagement and personalization. Low latency and reliable performance also characterise the system making it possible to implement the system in the real world. On the whole, the suggested system is efficient in overcoming the drawbacks of the traditional recommendation systems, combining the advanced methods with a rough-scaled system and a non- sluggish architecture.

References

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Copyright

Copyright © 2026 T Deva Harsha Vardhan, M Bharat Srivastava, K. Harshini, Dr. D Sreenivasulu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.