Intelligent Music Recommendation System Based on NLP Technology: Practice of Integrating Flask and Machine Learning

This article introduces an intelligent music recommendation web application integrating the Flask framework, machine learning, and natural language processing (NLP) technologies. It aims to address the limitations of traditional recommendation systems in cold start and content feature capture, and explore how AI can deeply understand users' music preferences. The project demonstrates the lightweight integration of Flask and AI technologies, covering key content such as core applications of NLP in music content analysis, machine learning model selection, and user experience design.

In the era of digital music streaming, users face the problem of choice overload, with platforms like Spotify and Apple Music adding tens of thousands of new songs daily. Traditional collaborative filtering recommendation methods are effective but have cold start issues and struggle to capture music content features. In recent years, breakthroughs in NLP technology have brought new possibilities to music recommendation—by analyzing song metadata, lyrics, user comments, etc., AI can deeply understand music semantics and emotions, providing precise recommendations.

The project uses Flask as the web framework; its lightweight nature facilitates rapid prototype building and focuses on the implementation of recommendation algorithms. The system follows the MVC architecture: the model layer handles machine learning inference and NLP processing, the view layer manages front-end display, and the controller layer coordinates requests and business logic. The layered design makes the code structure clear, which is conducive to subsequent expansion and maintenance.

The technical highlight of the project lies in the in-depth application of NLP:

1. Lyric Semantic Analysis: Understand the emotion, theme, and style of lyrics through word embedding and topic modeling;
2. Text Feature Extraction: Extract feature vectors from lyrics, song descriptions, and user comments using technologies like TF-IDF and BERT;
3. Semantic Similarity Calculation: Calculate semantic similarity between songs based on text features to capture associations of abstract concepts (e.g., songs suitable for running or late-night emo).

The core of the recommendation algorithm is content-based filtering, which combines NLP to convert unstructured text into feature vectors. The system may integrate collaborative filtering or matrix factorization technologies to form a hybrid strategy that balances personalization and diversity. Model training requires data such as user play records, favorites, and ratings; in cold start scenarios, content-based recommendation is used as a fallback, and it switches to a personalized model as interaction data accumulates.

User Experience Design:

• Real-time feedback: Capture user operations (skip, favorite) in time to optimize recommendations;
• Explainable recommendations: Inform users of the reasons for recommendations (e.g., liking the lyric style of a certain singer) to enhance trust;
• Diversity balance: Avoid filter bubbles and introduce exploratory recommendations.

Technical Challenges:

• Data copyright issues: Lyrics and audio feature data are protected by copyright;
• Real-time requirements: Model inference needs low latency;
• NLP processing difficulties: Music text contains non-standard language such as slang and colloquial expressions;
• Complex model evaluation: Music taste is subjective and time-sensitive.

Application Scenarios:

• Individual users: Discover favorite music;
• Music platforms: Improve user retention and engagement;
• Creators: Understand market trends and audience preferences.

Future Expansion:

• Introduce audio signal processing to implement multi-modal recommendations;
• Integrate large language models to provide conversational recommendations;
• Combine user context (time, location, activity) to implement context-aware recommendations.

This project represents a typical paradigm of integrating Flask and AI technologies. It builds a practical intelligent recommendation application through the semantic understanding capability of NLP, providing learning references for engineers who are new to recommendation systems or NLP development. With the development of multi-modal AI technologies, future music recommendation systems will be more intelligent and personalized.