Presentation is loading. Please wait.

Presentation is loading. Please wait.

Finding Similar Music Artists for Recommendation Presented by :Abhay Goel, Prerak Trivedi.

Similar presentations


Presentation on theme: "Finding Similar Music Artists for Recommendation Presented by :Abhay Goel, Prerak Trivedi."— Presentation transcript:

1 Finding Similar Music Artists for Recommendation Presented by :Abhay Goel, Prerak Trivedi

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)http://developer.yahoo.com/music/ 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’s Similarity 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’s Similarity coefficient between |ax| and ac as defined in formula below. o α represents the total number of genres that is not presented in |ax| but appears in ac o β represents the total number of genres that is not presented in ac but appears in |ax| o γ represents the total number of genres presented in both |ax| and ac o |ax| represents the genre which the artist belongs to, where a x (g y ) = 1

8 Computing User Ratings for Artist The User Rating (R a x ) 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_a x of users, who give rating to artist a x, and na x is the number of users in ru_a x

9 Computing New Similarity Level The Similarity Level (SL 2 ) is recalculated by combining the result from SL and Ra x using the formula as follows: o W SL1 is weight for the corresponding similarity level in Equation for SL o W Ra is the weight for the corresponding artist rating in Equation for Ra x

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: o kNN(q) query retrieves the k nearest-neighbors of the object q o 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


Download ppt "Finding Similar Music Artists for Recommendation Presented by :Abhay Goel, Prerak Trivedi."

Similar presentations


Ads by Google