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?