Project Overview
Project Type:
Role
Project Outcome
Capstone project on a Music Recommendation System using collaborative filtering techniques.
Data Scientist / Machine Learning Engineer
1. Developed a recommendation system to propose the top 10 songs for users based on their listening history.
2. Implemented Collaborative Filtering (user-based, item-based, matrix factorization, cluster-based) with hyperparameter tuning to improve prediction accuracy.
3. Implemented Content-based Recommendation System
4. Achieved a strong F1 score, demonstrating the model's predictive power.
Methods
1. Data discovery and preparation using Million Song Dataset.
2. Implemented collaborative filtering based on user similarity (Cosine Similarity).
3. Filtered and cleaned data for improved model performance.
Deliverables
1. Recommendation model capable of suggesting personalized song playlists.
2. Final report on model performance metrics (e.g., F1score, precision, recall).
3. Suggestions for model improvements (e.g., incorporating mood tags, producer credits).
Tools
Python, Pandas, Scikit-learn, nltk, Surprise, Matplotlib, numpy, Seaborn.
Context
Music Recommendation System:
A machine learning system to provide personalized music recommendations based on user listening history
Objective
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In an increasingly digital world, users spend substantial time on music streaming platforms like Spotify. Providing tailored music recommendations enhances user engagement and satisfaction.
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The system leverages User-Based Collaborative Filtering to recommend songs based on user preferences and the listening habits of similar users.
Key Research Questions
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How effective are the recommendations in terms of accuracy and user satisfaction?
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What metrics can be used to assess the success of the recommendations (e.g., F1 score, precision, recall)?
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Can the model be improved by incorporating additional features like genre or mood tags?
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How do these features impact the precision of song recommendations?
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How scalable is the model for handling large datasets with millions of users and songs?
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How does the system perform when scaled to real-world usage levels (like a platform with millions of users)?
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PHASES OF PROJECT
This Music Recommendation System project progressed through six phases: Data Discovery, Data Preparation, Model Planning, Model Building, Communicating Results, and Operationalizing the system for real-world use. Each phase was crucial in developing and optimizing the recommendation model.
Data Discovery
Goal:
Identify and gather relevant data to build the music recommendation system.
Data Preparation
Goal:
Clean and prepare the data for further analysis.
Model Planning
Goal:
Decide on the most appropriate algorithm to build the recommendation system.
Model Building
Goal:
Develop and train the recommendation model.
Communicating Results
Goal:
Present the model’s performance and insights to stakeholders.
Operationalize
Goal:
Deploy the model in a real-world environment and monitor its performance.
Data Discovery
Phase-1
1 / Datasets:
The project utilized the Million Song Dataset which consists of two main files:
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song_data: Provides metadata such as song_id, title, artist_name, year, and release.
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count_data: Contains user interaction information including user_id, song_id, and play_count.
2 / Initial Data exploration
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involved checking for missing values and assessing the overall size of the dataset to identify trends and the types of recommendations that would be relevant.
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Exploratory analysis was performed to check the number of unique users, songs, and interactions between them. This helped establish the scale and scope of the recommendation problem.
Key Insights
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The dataset was sufficiently large, with millions of rows, providing a rich base for building a recommendation model.
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There were many unique songs and users, creating ample opportunities for collaborative filtering approaches, where similar users could be identified based on their listening habits.
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Missing values in song_data, particularly in the title and release fields, were observed early on. These missing values could affect the accuracy of any recommendation system that leverages song metadata.
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A basic structure of user-song interactions was identified. Users had different listening patterns, ranging from very few interactions to those who engaged with hundreds of songs.
Challenges
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Data Sparsity: A significant number of users had interacted with only a small subset of songs. This imbalance introduced challenges in ensuring that there would be enough data to build reliable recommendations for less active users.
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Missing Data: The presence of missing metadata for some songs (e.g., missing titles and release years) could reduce the effectiveness of any content-based enhancements that might be considered later in the project.
Data Preparation
Phase-2
1 / Process:
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The Data Preparation phase involved cleaning, merging, and transforming the data into a format suitable for building the recommendation model.
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First, the song_data and count_data datasets were merged on the song_id field. This combined user interactions with song metadata.
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Missing values in song_data (e.g., missing title and release data) were either dropped or imputed where necessary.
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Label Encoding was applied to the user_id and song_id fields, converting these categorical identifiers into numerical values that could be used for model training.
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Filtering was done to remove noise from the dataset. Specifically, users who had interacted with fewer than 90 songs and songs that had been listened to by fewer than 120 users were excluded. This helped ensure the dataset was focused on the most active users and the most popular songs.
Key Insights
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Merging Data: Merging the two datasets successfully combined user interaction data with song metadata. This allowed the system to leverage both behavior-based (collaborative filtering) and content-based (song metadata) information if needed.
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Data Filtering: The data quality improved significantly by removing users with very few interactions and songs with low play counts. This filtering step helped mitigate the impact of sparse data, focusing the model on active users and frequently played songs.
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Label Encoding: Converting user_id and song_id into numeric format was essential for collaborative filtering, as many machine learning models require numerical inputs. Label encoding successfully handled the large number of unique users and songs.
Challenges
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Sparsity Management: Even after filtering, there were still instances where certain songs or users had very few interactions. This created difficulties in generating accurate recommendations for all users, especially those with minimal activity.
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Imputation of Missing Values: Deciding whether to drop rows with missing metadata or impute values was a trade-off. Dropping too much data would reduce the size of the dataset, while improper imputation could introduce bias.
Phase-3
1 / Process:
Three primary recommendation techniques were chosen:
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Rank-Based Recommendation: Recommendations were made by ranking songs based on their global popularity (e.g., play counts).
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Collaborative Filtering: Using both user-based and item-based collaborative filtering methods to recommend songs based on similar users' preferences or similar songs.
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Content-Based Recommendation: Leveraged song metadata, such as artist and genre, to recommend songs based on features associated with a user’s previous choices.
Model Planning
Key Insights
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Collaborative Filtering: This method was divided into User-User and Item-Item similarity. The Surprise library was used to build these models, which gave personalized recommendations based on users' play history.
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Content-Based: By considering additional song features (e.g., artist, genre), this method allowed the model to make more personalized recommendations beyond just play counts.
Challenges
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Cold Start Problem: Users with very few interactions or new songs faced difficulties with collaborative filtering. Content-based methods were more effective in handling this challenge.
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Balancing Precision and Recall: Ensuring the model provided relevant recommendations (precision) while covering a wide range of possibilities (recall) required careful tuning.
Model Building
Phase-4
1 / Rank-Based Model:
Implemented a straightforward ranking system that recommended the most popular songs across all users. This method was simple but effective as a baseline.
2 / Collaborative Filtering:
Both user-user and item-item collaborative filtering were implemented using cosine similarity and matrix factorization (SVD). Hyperparameter tuning was performed to optimize the number of neighbors (k) and similarity thresholds.
3 / Cluster-Based Model:
I explore the similarities and differences in people's song tastes based on how they rate different songs. I clustered similar users together and recommended songs to a user based on play_counts from other users in the same cluster.
4 / Content-Based Model:
A content-based filtering model was built using song metadata to recommend songs based on features like artist, genre, and release year.
Key Insights
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Collaborative Filtering: User-user similarity with hyperparameter tuning provided the highest F1 score of 0.485, outperforming item-based filtering.
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Item to Item filtering models with and without hyperparameters have the largest discrepancies between F_1 score values. This indicates that tuning the model can significantly impact your model performance. Matrix factorization considers the user to item filtering, allowing for a more personalized recommendation system strictly based on the user's past behaviors. It was the second-highest F_1 Score.
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Rank-Based: While simplistic, rank-based methods performed decently for popular songs, though they lacked personalization.
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Content-Based: This model improved recommendations for niche songs and new users with limited interaction history by considerin.
Challenges
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Scalability: Collaborative filtering, especially user-user similarity, would be computationally expensive due to the large number of pairwise comparisons.
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Tuning Parameters: Finding the right balance between precision, recall, and the number of nearest neighbors (k) was critical to improving the model’s performance.
Communication Results
Phase-5
1 / Process:
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Performance metrics like RMSE, Precision, Recall, and F1 Score were used to evaluate and compare the models.
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Results were visualized and communicated to stakeholders, highlighting the advantages of using a multi-method approach for personalized recommendations.
Key Insights
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After completing two individual recommendation systems I realized that the most appropriate model depends on the data that is provided. No one model works for every recommendation system. The most appropriate model is determined through what can provide the most accurate prediction rating and this varies based on the business ask.
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Hybrid Approaches: Combining collaborative filtering with content-based approaches helped mitigate the cold start problem and improve recommendation accuracy.
Challenges
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Interpretability: Explaining the technical aspects of collaborative filtering (e.g., SVD, matrix factorization) to non-technical stakeholders required simplifying the concepts while still conveying the model’s effectiveness.
Phase-6
1 / Process:
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The final recommendation system was prepared for deployment, ensuring it could handle real-time song recommendations for a large user base.
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To improve performance, I would like to organize additional data points. Additional data may include genre, mood tags, and producer credits. Adding these aspects into models will help clarify a more direct recommendation without adding too much noise. Mood tags can help group songs into themes. Producers tend to write similar styles of songs; thus, producer credits can recommend more songs not based on artist popularity but on producer popularity.
Operationalization
Key Insights
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Scalability: To handle millions of users, parallel processing or distributed systems were recommended for large-scale implementation.
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Cold Start Solutions: The content-based model was particularly useful for cold start problems, and a hybrid system was suggested to improve the overall user experience by combining collaborative filtering and content-based methods.
Challenges
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Real-Time Deployment: Ensuring that the system could generate real-time recommendations for users without significant delays required further optimization of the algorithms.
Reflection
This project taught me that data preprocessing is like prepping veggies for a stir-fry—skip it, and things get messy. Experimenting with Rank-Based, Collaborative Filtering, and Content-Based approaches showed that no one-size-fits-all in recommendation systems, much like finding the right playlist for every mood. I also realized that battling data sparsity and cold start issues is like trying to guess someone’s favorite song at a party—you just need more clues! Overall, it sharpened my data skills and reminded me that a good model, like a good playlist, always needs a little fine-tuning.