Music Recommendation System is a cutting-edge technology that uses algorithms to analyze user preferences and behavior in order to suggest personalized music recommendations.
A music recommendation system project is a fascinating endeavor that combines elements of data analysis, machine learning, and user experience design to create personalized music suggestions for users. Here's a breakdown of what such a project might entail:
Data Collection: The first step is gathering data. This could include information about songs, albums, artists, genres, user preferences, listening history, and more. APIs from music streaming platforms like Spotify or Last.fm are often used to access this data.
Data Preprocessing: Once the data is collected, it needs to be cleaned and preprocessed. This involves tasks like removing duplicates, handling missing values, and converting data into a suitable format for analysis.
Feature Engineering: Features are attributes or characteristics of the data that can be used to make predictions or recommendations. In the context of music recommendation, features could include song tempo, genre, artist popularity, user listening history, and so on. Feature engineering involves selecting and creating relevant features from the data.
Model Selection: There are various machine learning algorithms that can be used for recommendation systems, such as collaborative filtering, content-based filtering, matrix factorization, and deep learning models. The choice of model depends on factors like the type of data available and the specific requirements of the project.
Training the Model: Once a model is selected, it needs to be trained on the preprocessed data. During training, the model learns patterns and relationships in the data that enable it to make accurate recommendations.
Evaluation: After training the model, it's important to evaluate its performance. This involves using metrics like precision, recall, and accuracy to assess how well the model predicts user preferences and provides relevant recommendations.
Deployment: Once the model is trained and evaluated, it can be deployed into a production environment where users can interact with it. This might involve integrating the recommendation system into a music streaming platform or building a standalone application.
Feedback Loop: A good recommendation system should be able to adapt to changing user preferences over time. This requires implementing a feedback loop where user interactions with the system are continuously monitored and used to update the model and improve the recommendations.
Throughout the development process, it's important to consider factors like scalability, usability, and privacy to ensure that the recommendation system is both effective and user-friendly. Additionally, incorporating techniques like A/B testing can help optimize the system and refine its recommendations based on real-world user feedback.
3. Background and Motivation
Growth in Music streaming consumption among consumers
Plethora of options available - Spotify, Pandora, 8tracks
*http://www.riaa.com/reports/riaa-2015-year-end-sales-shipments-data-report-riaa/
RIAA - Recording Industry Association of America
4. Background and Motivation
Music Recommendations - excellent feature for any music application.
Better Recommendations - Better Conversions, More engagement
Develop a music recommendation system based on the Million Song
Dataset using various recommendation methodologies and draw a
comparative analysis between them
5. Dataset Description
The Million Song Dataset User Taste Profile
Dataset
Freely available collection of audio features and metadata 48 million triplets(User Id, Song ID, count)
for a million contemporary popular music tracks ( 280 GB) Gathered from 1 million users
1 M songs.
Subset: 2.8 GB (compressed) -> 10 GB (CSV) Size: 500 MB (compressed) -> 3 GB (.txt
file)
7. Collaborative Filtering
The Collaborative Filtering method uses previous user choices, and
choices of similar users to predict the possible future song
selections
Data was first fed through a data cleaning module, which removed
erroneous entries, such as missing values for both Song and User
ID’s.
ID’s were then mapped to a unique integer via a dictionary as Spark
functions require numeric values
8. Collaborative Filtering
Used Matrix Factorization instead of the conventional distance
function
Latent factor methods were used to train on some known data
K User Features (latent) were extracted , which in this case was the
song count
Assumption-Song Count represents all the factors that could have
contributed to a user choosing to listen to a song
10. Content Based Recommendation
We extracted certain features from the dataset, which describes
features of a song
Normalized those features by taking its product with its confidence to
get a final value, such as mode and mode confidence to get a final
mode estimate
Removed features not related to audio features
Clustered songs in a higher dimensional space, and found similar
songs within each cluster
11. Content Based Recommendation
Training Method- Cross Validation
User-triples dataset was split in a ratio of 80:20.
80% of the dataset was used to train a clustering model
Created a profile for each user by merging the user-song dataset
Each user profile consisted of a “mean” of all songs heard by a user in
his lifetime (as per the dataset)
Clustered using K-means
13. Results - Content Based Filtering
Precision = True Positives / (True Positive + False Positive)
Final Precision- 6.1% on entire MSD subset
Performance for Content Based decreases with increase in size of data
*
* http://www-
personal.umich.edu/~yjli/content/pro
jectreport.pdf
16. Team Work
- Scoping the project, data extraction, data cleaning - All
- Collaborative Filtering - Sharang
- Content Based Filtering - Piyush
- Trend Analysis & Visualizations - Prachi, Snigdha
- Conclusion, Report, Presentation - All
- Asking questions on Piazza - Anonymous
17. Conclusion
Eye opener on big data and its difficulties
We were fairly satisfied with our results, we managed to reduce the
RMSE by almost 1, and virtually predicted how many times a user
will play a song
We fairly satisfied with a precision of 6.1 % considering we tried a new
recommendation methodology
Collaborative Filtering was easier to implement and evaluate
