Recommender Systems for Last.fm

Recommender systems with collaborative filtering created with Apache Mahout framework. The system uses a Music Recommendation dataset for research purposes as input, but you can train it and predict recommendations with any other dataset. This project explores the calibration and accuracy of user-based and item-based models.

Data

The original dataset contains <user, timestamp, artist, song> tuples collected from Last.fm API, using the user.getRecentTracks() method. This dataset represents the whole listening habits (till May, 5th 2009) for nearly 1,000 users.

The pre-processed dataset below contains <user, artist, rating> tuples. The rating field was calculated by normalizing the number of times a user listened to a specific artist’s songs in Last.fm.

Table format: u.data.csv

You can see the original dataset here

Model Tuning

• The training results of the user-based collaborative filtering model are shown below:

• The training results of the item-based collaborative filtering model are shown below:

Technologies and Techniques

• Java (JDK 1.7)
• Eclipse IDE
• Apache Mahout
• Maven dependencies

Program Execution Rules

The project has an executable in the ‘jar’ folder. The JAR name is: RS_CF_LastFm-v1.jar and you must send as input parameters:

• User ID for which recommendations are to be computed
• Filepath to the input data
• Filepath to the output file
• Type of collaborative filtering: USER or ITEM
• Similarity metric: COSINE, PEARSON or JACCARD
• Number of neighbors for the KNN algorithm (only applies with user-based filtering). Default value: 51
• Desired number of recommendations. Default value: 10

Execution examples:

    java -jar RS_CF_LastFm-v1.jar 1 ../data/in/u.data.csv ../data/out/output.txt USER COSINE 101 20
    java -jar RS_CF_LastFm-v1.jar 10 ../data/in/u.data.csv ../data/out/output.txt ITEM PEARSON 0 10
    java -jar RS_CF_LastFm-v1.jar 10 ../data/in/u.data.csv ../data/out/output.txt ITEM JACCARD

The .JAR program must be run with Java 7 or higher.

Program Output

Once trained the model, the system can make recommendations (on demand) for users, as follows:

Conclusions

1. Apache Mahout is an excellent fast development framework for Recommender Systems projects. It has a wide variety of Machine Learning algorithms to make predictions (recommendations) and an extensive list of similarity functions.
2. In static or semi-static scenarios, recommendation systems with items-based collaborative filtering offer better results than those users-based , since it is easier to calculate the similarity between items than between users.
3. As a general rule, both in the user-based and in the item-based models, the predictive results improved when the models were exposed to more data (75/25 or 80/20), since the Machine Learning algorithm used has more information from which to learn.
4. The construction of a model items-based takes more time than the construction of a user-based model. However, once the model is built, it makes predictions more quickly and above all, more accurately than the user-based one.
5. When a new user is created, the user-based recommender model will have a cold start for that user, until the user performs enough interactions to be able to look like someone else. Analogously, it occurs for the item-based model when a new item is created.

Contributing and Feedback

Any kind of feedback/criticism would be greatly appreciated (algorithm design, documentation, improvement ideas, spelling mistakes, etc…).

Authors

• Created by Andrés Segura Tinoco and Francisco Ariza Benavides
• Created on May 29, 2019

License

This project is licensed under the terms of the MIT license.

Acknowledgements:

Thanks to Last.fm for providing the access to this data via their web services.