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Finding Similar Music Artists for Recommendation

Finding Similar Music Artists for Recommendation. Presented by : Abhay Goel , Prerak Trivedi. Introduction. In this project, we present similar music artists based on their genre, social ratings and the artist’s era

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Finding Similar Music Artists for Recommendation

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  1. Finding Similar Music Artists for Recommendation Presented by :AbhayGoel, PrerakTrivedi

  2. Introduction • In this project, we present similar music artists based on their genre, social ratings and the artist’s era • We find the related contents using information retrieval from the Y! webscope dataset (for ratings) and Y!Music database. • It is much like Similar video recommendations. However, that works on finding similar videos based on visual and textual similarity where as this works on more parameters such as user ratings, artist era and the genre.

  3. Technologies Deployed • MySQL – To store the Yahoo! Webscope dataset R1- For music artists and ratings.(Approx 97,000 artists and 3.25 million User ratings ) • Yahoo Music API/JSON (http://developer.yahoo.com/music/) – For getting artist Genre and Era(5000 queries per day per IP address) • JSP/JSON – Connectivity between MySQL and HTML, to get User Ratings • Apache Tomcat – Web Server used for JSP pages • HTML/JavaScript /AJAX– Front end UI for entering artist query and displaying results • SQL– Executing of queries.

  4. Model Approach • Gather Information about artist from Y!Music • Use the Y! Music API and an application ID, identify to access the data. • The API is REST-based and returns the data in JSON,XML format. • Collected data includes: artist information, category, releases, top similar artists, top tracks, era and events. • Use the Ratings data from the Y! Webscope dataset and apply the algorithm.

  5. Method / Algorithm • We implement three steps of computation in order to obtain more detailed results, as defined below: • Compute similarity measure based on artist’s genre and era (Using Jaccard’sSimilarity Coefficient and Nearest Neighbor Search) • Construct external db from the webscope dataset to evaluate the artist’s ratings • Combine the similarity measure and the artist reputation for the improved searching of the similar artists.

  6. Computing Artist Information • Consider genre and album releases as basis for computing similar artists using these steps: • a. Applying comparison between artists’ genre using distance measure • b. Applying user rating to artists • c. Applying Nearest-Neighbor search on artists’ releases album • Table: Parameter Properties

  7. Computing Similarity Measure • The Similarity Level (SL) between the Artist and Genre is the Jaccard’sSimilarity coefficient between |ax| and ac as defined in formula below. • α represents the total number of genres that is not presented in |ax| but appears in ac • β represents the total number of genres that is not presented in ac but appears in |ax| • γ represents the total number of genres presented in both |ax| and ac • |ax| represents the genre which the artist belongs to, where ax(gy) = 1

  8. Computing User Ratings for Artist • The User Rating (Rax) is calculated from the Yahoo! Webscope Dataset R1 and is the average score for that particular artist for every user rating received. • The above formula represents a set of ru_ax of users, who give rating to artist ax, and nax is the number of users in ru_ax

  9. Computing New Similarity Level • The Similarity Level (SL2) is recalculated by combining the result from SL and Rax using the formula as follows: • WSL1is weight for the corresponding similarity level in Equation for SL • WRais the weight for the corresponding artist rating in Equation for Rax

  10. Computing Nearest-Neighbor Search • The Nearest Neighbor Search (kNN(q)) is performed to find the closest object to the given query object q. The general equation to find the k nearest neighbors is as follows: • kNN(q) query retrieves the k nearest-neighbors of the object q • R is the distance range (r) used for searching, where p Є S with d(q, p) ≤ r.

  11. PROJECT RESULTS WITH SCREENSHOTS

  12. Screenshot of Y! Webscope dataset from MySql Database for ratings

  13. Screenshot of Data Collected from Yahoo Music API

  14. User Interface Screenshot(1)

  15. User Interface Screenshot(2)

  16. User Interface Screenshot(3)

  17. DEMO

  18. References • -Reference from Yahoo Webscope Publication “Finding Similar Music Artists for Recommendation”. • -Yahoo Developer Network

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