LYRIC-BASED ARTIST
NETWORK
Derek Gossi
CS 765
Fall 2014
The Big Problem
How do we make better
music recommendations?
The Big Problem
How do we make better
music recommendations?
Personalized recommendations
Anonymous recommendations based on similarity
Playlist generation
The Big Problem
How do we make better
music recommendations?
Ideally: Understand all the factors
which link songs or artists together
Topics
• Background on Music Recommendation
• The Dataset
• Existing Research
• Proposed Research
BACKGROUND
ON MUSIC RECOMMENDATION
Music Recommendation Systems
Approaches to Recommendation
• Collaborative Filtering
• Users that liked this artist/song also liked that artist/song
• Amazon, iTunes store, Spotify
• Tagging
• Categorization based on user-generated or pre-defined tags
• Calm, sad, romantic, cheerful, anxious, depressed
• Last.fm
• Content-based
• Look at the audio signal
• Not widely used in industry yet
• Pandora, Spotify (in progress)
• What can the lyrics tell us?
Approaches to Recommendation
The Problem with Tags
Care vs. Scale
B Whitman, Co-Founder of The Echo Nest, “How music recommendation works—and doesn’t work”
Care vs. Scale
B Whitman, Co-Founder of The Echo Nest, “How music recommendation works—and doesn’t work”
Comparison of Approaches
• Collaborative filtering is widely used in practice
• Precision vs. Profit
• Even though you might like x better, Amazon makes more money by
recommending y
• Probably less of an issue for subscription services such as Spotify
• Existing recommendation systems largely do not take
content of music into account
• Why?
• Possibility for large error
• Computational cost
• Still being researched
MIR (Music Information Retrieval)
• Emerging area of research
• Gathering information directly from audio signal
• Success in determining tempo, key, and loudness
• Research in time signature tracking, melody detection
MIR (Music Information Retrieval)
• What about trying to predict
location on reduced-dimension
latent space of users and songs
using audio features?
• Deep learning methodologies
The Question
• Can lyrics be used to improve recommender systems?
• Benefits of lyrical analysis approach
• Known factors make for easy error checking
• Large-scale factors such as repetition or key words are easy to
compute
• Nearly as scalable as pure audio analysis for most popular genres
• Disadvantages of lyrical analysis approach
• Not all songs have lyrics!
• Text analysis is a subtle and complex problem too
• Audio + lyrics make for new interpretations
• Reducing to artist level will “average out” some error
• A combined approach will likely be the best approach
Care vs. Scale
B Whitman, Co-Founder of The Echo Nest, “How music recommendation works—and doesn’t work”
Care vs. Scale
Lyrical analysis
B Whitman, Co-Founder of The Echo Nest, “How music recommendation works—and doesn’t work”
Care vs. Scale
Lyrical analysis +
audio analysis +
CF
Lyrical analysis
B Whitman, Co-Founder of The Echo Nest, “How music recommendation works—and doesn’t work”
THE DATASET
The Million Song Dataset (MSD)
Million Song Dataset
• Open source dataset released in Feb 2011
• Metadata and audio features for a million contemporary
audio tracks
The Million Song Dataset Challenge
• Online competition
• Given full listening history for 1 million users
• Given half of the listening history for 110,000 users
• Goal: predict the other half of the listening history
• Metric: mean average precision
• Best ranked teams used some form collaborative filtering
• See F. Aiolli, “A Preliminary Study on a Recommender
System for the Million Song Dataset Challenge”
The Million Song Dataset Challenge
EXISTING RESEARCH
A Summary
Network Topology
• P. Cano, O. Celma, and M. Koppenberger. “The topology
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of music recommendation networks,” Feb 2008.
Analyzes four music recommendation systems from a
network perspective
Directed edges
n = 16,302 (Yahoo) to 51,616 (MSN)
m = 158,866 (AMG) to 511,539 (Yahoo)
Small-world properties in all networks
• Average shortest path < 8
• Clustering coefficient from 0.14 (Amazon) to 0.54 (MSN)
Lyrical Analysis
• X. Hu, J. S. Downie, and A. F. Ehmann. “Lyric text mining
in music mood classification,” 2009.
• 2,829 unique audio tracks from last.fm with lyrics and tags
• Tags grouped into 18 distinct categories
• calm, comfort quiet, serene, mellow, chill out, …
• grief, heartbreak, mournful, sorrow, sorry, …
• Objective: predict tag category
• Lyrical model, audio feature model, and combined model
• Lyrical features were found to outperform audio in cases
Lyrical Analysis
• Y. Xia, K. Wong, L. Wang, and M. Xu. “Sentiment vector
space model for lyric-based song sentiment
classification,” June 2008.
• Custom sentiment vector space model (s-VSM) used to
classify 2,653 Chinese pop songs
• Only two classes: light-hearted and heavy-hearted
• Lyrics found to outperform audio features in the
classification problem
PROPOSED RESEARCH
Proposed Research
• Use the MSD to create a network of songs and artists
linked by threshold lyrical similarity
• Metric of similarity will be based on:
• Use of key words or key word groups
• Word complexity and range of words used
• Sentiment
• Random sample will need to be used, as mapping full
dataset would require ~750,0002 iterations
• Cluster the network into n distinct “communities”
• Unsupervised approach
Research Questions
• Network properties?
• Scale, clustering, etc.
• What are the most natural communities?
• Genre, mood, complexity?
• How does it compare to existing models?
• How much error is introduced by using lyrics only?
• How does the network topology of artists linked by lyrical
similarity possess compare to existing user-based
collaborative filtering networks?
• Can it be used to improve music recommendation?
QUESTIONS?