Music recommendations model using natural language processing

Presentation on music recommendations project with code snippets. The project works on NLP and provide music recommendations

1. Artificial Intelligence and Machine Learning (21CS54) “ MUSIC RECOMMENDATION USING MACHINE LEARNING” Presented By : Nayeem Afif Shaikh (1BI21IS061) Saanya Shukla (1BI21IS078) Udhav Gupta (1BI21IS102) Akanksha Singh (1BI21IS131) Bangalore Institute of Technology Department of Information Science & Engineering Faculty Incharge : Dr. Roopa H Associate Professor Dept. of ISE, BIT

2. Contents Problem Statement Introduction Objectives Datasets Techniques Used Text Cleaning/Text Processsing Feature Extraction Similarity metrics Recommendation Techniques Serialization of data Future Enhancements 13. Conclusion

3. Problem Statement Develop a machine learning model for music recommendation leveraging tokenization techniques to process user preferences efficiently. The objective is to create a system that analyzes user behavior and tokenizes musical features, mitigating sparse data challenges. The model must navigate issues like overfitting and underfitting, ensuring adaptability to diverse musical tastes and generalization to recommend songs across both popular and niche genres. Employ tokenization to represent musical elements as input features, facilitating effective learning and personalized playlist generation. Scalability is crucial to accommodate an expanding music library, while real-time responsiveness ensures an engaging, tailored music discovery experience for users .

4. INTRODUCTION You love listening to music right? Imagine hearing your favorite song on any online music platform let’s say Spotify. Suppose that the song’s finished, what now? Yes, the next song gets played automatically. Have you ever imagined, how so? What is the logic or piece of code behind this? The same case might happen to you while watching a movie on Netflix or buying something from Amazon. You get recommendations. Precisely, these recommendations are system generated and the logic behind them is nothing but Machine Learning. Machine Learning is the ability to make machines learn and act.

5. You search for some songs and listen to them and this is how the machine learns. It then recommends songs to you on the basis of various factors like singer or composer, movie, tone of the song, whether it is romantic or disco, it is acoustic or original, etc. A recommendation system is a kind of filtering system which predicts the preferences a user might give based on his/her activity. Machine learning plays a super vital role in building these systems. This technique is widely popular and practiced in every streaming platform like YouTube, Amazon, Netflix, Spotify, Tidal, etc. You don’t have to play songs manually, instead, a music recommendation system will do that job for you.

6. Develop a Robust Recommendation Model: Implement a machine learning model capable of providing accurate and personalized music recommendations based on user preferences. Utilize Advanced Feature Representation Techniques: Apply tokenization and other advanced feature representation techniques to capture nuanced musical elements, enhancing the model's ability to understand and recommend diverse music genres. Incorporate User Behavior Analysis: Integrate user behavior analysis into the model to ensure a dynamic and evolving understanding of individual preferences, fostering accurate and real-time recommendations. OBJECTIVES

7. DATASETS

8. TECHNIQUES USED Text Processing and NLP Feature Extraction Similarity Metrics Recommendation Techniques Serialization of data

9. TEXT CLEANING/TEXT PREPROCESSING Text cleaning in machine learning typically falls under the broader topic of "Data Preprocessing" or "Data Cleaning." Data preprocessing is an essential step in the machine learning pipeline, where raw data is transformed, cleaned, and formatted to make it suitable for training machine learning models. Text cleaning is crucial for improving the quality of input data and reducing noise that may negatively impact model performance. It helps in standardizing the text representation and reducing the dimensionality of the feature space, making the data more manageable for machine learning algorithms. Proper text cleaning enhances the effectiveness of subsequent NLP tasks such as sentiment analysis, text classification, named entity recognition, and topic modeling.

10. FEATURE EXTRACTION Feature extraction in a music recommendation system is pivotal for translating raw music data into a format that machine learning models can effectively process. Various strategies are employed to derive pertinent features: Metadata Features provide supplementary information associated with music tracks, encompassing artist details (genre, nationality, etc.), album information (release year, record label), track duration, and textual data such as song titles and lyrics. Collaborative Filtering Features leverage user-item interaction data, comprising user listening histories, preferences, playlists, and item popularity metrics, to generate personalized recommendations based on user behaviour patterns.

11. SIMILARITY METRICS In music recommendation systems utilizing machine learning, similarity metrics play a crucial role in determining the likeness between songs, artists, or users. Cosine Similarity: This metric measures the cosine of the angle between two vectors, representing the similarity in their direction. In the context of music recommendation, cosine similarity can be applied to compare feature vectors derived from audio features, metadata, or user preferences. Euclidean Distance: It calculates the straight-line distance between two points in a multi-dimensional space. In music recommendation, Euclidean distance can quantify the dissimilarity between songs or artists based on their feature representations.

12. RECOMMENDATION TECHNIQUES In music recommendation systems leveraging machine learning, a variety of techniques are employed to generate personalized recommendations tailored to the preferences and behaviors of individual users. These techniques encompass collaborative filtering , content-based filtering, hybrid approaches, and sequential recommendation methods: Collaborative Filtering techniques leverage the collective wisdom of user interactions to make recommendations. Content-Based Filtering techniques focus on the intrinsic characteristics of music items and users' preferences to generate recommendations. Hybrid Recommendation approaches combine collaborative filtering and content-based filtering techniques to exploit the strengths of both methods. Sequential Recommendation methods consider the temporal dynamics of user interactions and recommend items based on sequential patterns in user behavior.

13. SERIALIZATION OF DATA In music recommendation systems powered by machine learning, serialization of data plays a vital role in storing, retrieving, and sharing trained models, as well as preserving user preferences and interaction histories. Several techniques are commonly employed for data serialization: Model Serialization: Trained machine learning models are serialized to disk in formats such as pickle, JSON, or HDF5. Feature Serialization: Extracted features from audio signals, metadata, or user interactions are serialized to preserve their representations in a compact and portable format. User Preferences Serialization: User preferences, such as listening histories, ratings, and playlists, are serialized to maintain personalized recommendations for individual users.

14. IMPLEMENTATION

15. OUTPUT

16. FUTURE ENHANCEMENT 1. Deep Learning Integration: - Explore advanced models like RNNs or transformers for intricate pattern recognition in user behavior and music content. 2.Context-Aware, Multi-Modal Recommendations- Enhance recommendations by considering user context (activity, location) and incorporating audio, lyrics, and user-generated content features. 3. Active Learning and Privacy-Preserving Techniques:* - Implement real-time adaptation through active learning and prioritize privacy-preserving methods like federated learning for user data protection.

17. CONCLUSION In summary, a music recommendation system, a subset of machine learning algorithms, utilizes big data to suggest music based on user interests and behavior. Employing techniques like factorization, vectorization, KNN, decision trees, and clustering, these systems can be collaborative, content-based, hybrid, or knowledge-based. In today's context, online music streaming services have surpassed traditional record purchases in popularity and effectiveness. These services leverage recommendation systems to enhance customer experience, providing a seamless and profitable interaction. For students and ML enthusiasts, building a music recommendation system serves as a challenging yet insightful exploration into the practical applications of machine learning, aligning academic knowledge with industry demands.

18. Thank You !!