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Music RecommendationBy Daniel McEnnis
Outline
• Sociology of Music Recommendation
• Infrastructure– Relational Analysis Toolkit
• Description• Evaluation
– GATE and Review Mining
Why do we like what we like?
• Personal Identity and Music– Music and Lifestyle Correlations
• Social Associations• Peer Groups• Content of the Music and Lyrics
– Culture specific understanding of music– Social meanings of musical forms– Ability to understand the lyrics
Social Networks and Music Recommendation
• What is social information?– Age and personal collections/preferences– Friends’ musical tastes– Opinions of local associations or groups– Local (geographical) opinions about music– Cultural background of the person
Where is the Data?
• Play-lists, personal music collections, and recorded listening habits
• Social network sites such as Facebook and Live Journal
• Web sites such as blogs and lists of favorite web pages
• Relationships between these artifacts
What Infrastructure is Needed
• Toolkit for synthesizing social data.
• Text mining tools for analyzing web-pages, music reviews, and blogs.
• Play-list analyzers• Content-based music
analysis toolkits
Social Toolkit Requirements
• Intuitive Java-Based Graph Toolkit
• Arbitrary multi-valued properties on nodes
• Social network analysis algorithms
• Efficient back-end processing
• Scripting support for experiments
Relational Analysis Toolkit (RAT)
• Low Level – Graph– Actor (Node)– Link (Arc, Edge)
• High Level – Collection of
algorithms– Scripting support
Exponential Similarity
-1.3k +2k 0
Music Recommendation
Degree Centrality
Djikstra Shortest paths
• Djikstra’s shortest path algorithm over this graph. Closeness measures are stored in a Path object cached at the graph object.
• Optimized version used inside Closeness and Betweeness for performance reasons.
Closeness Centrality
Betweeness Prestige
Page Rank
Kleinberg’s HITS
• Generates a set of ‘hubs’ (central actors) and ‘authorities’ (prestigious actors).
• Intuitively good hubs (User) point (Knows) to good authorities (User) and vice versa.
• Implemented in naïve and optimized versions.
Clique Definition
Evaluation
• How well can this method recreate a persons list of liked music
• 4% average precision
• 16% average recall
• Standard deviation > 100 for both– Sometimes it works really well, but often
doesn’t
Weka in RATArtist-User User Music Beatles BeachBoys Monkeys Metallica ListensTo
Beatles-A B + T T F F TC - F T T F TE 0 F F F F T
Beach Boys-A B + T T F F FC - F T T F FE 0 F F F F F
Monkeys-A B + T T F F FC - F T T F FE 0 F F F F F
Metallica-A B + T T F F FC - F T T F FE 0 F F F F F
Weka Evaluation
Same data as Ad Hoc algorithm
J48 Classifier
• 1% Precision
• 62% Recall
More coming….
Music Reviews - epinions.com
• Uses GATE Parts of speech analyzer
• Predicting positive/negative reviews
• Useful for tag extraction
• Negation problems
Conclusions
• Social information is important for music recommendation
• RAT has centrality algorithms, but requires more clustering and learning algorithms
• Music review mining ready for integration into the RAT environment
Future Work
• Evaluate with more Weka algorithms• Implement graph-based clustering algorithms• Implement other distance measures• Implement blog and web-page text mining• Integrate existing content based methods• Evaluate results with a user study
Questions?