Download 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
• 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
Technologies Deployed
•
MySQL – To store the Yahoo! Webscope dataset R1- For music artists
and ratings.
•
Yahoo Music API/JSON (http://developer.yahoo.com/music/) – For getting
artist Genre and Era.
•
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.
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.
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 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
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
Computing Similarity Measure
• The Similarity Level (SL) between the Artist and
Genre is the Jaccard’s 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 ax(gy) = 1
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
Computing New Similarity Level
• The Similarity Level (SL2) is recalculated by
combining the result from SL and Rax using the
formula as follows:
o W
o W
SL1
Ra
is weight for the corresponding similarity level in Equation for SL
is the weight for the corresponding artist rating in Equation for Rax
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.
PROJECT RESULTS WITH SCREENSHOTS
Screenshot of Y! Webscope dataset
from MySql Database for ratings
Screenshot of Data Collected from
Yahoo Music API
User Interface Screenshot(1)
User Interface Screenshot(2)
User Interface Screenshot(3)
DEMO
References
-Reference from Yahoo Webscope Publication “Finding
Similar Music Artists for Recommendation”.
-Yahoo Developer Network