Tim Leonardlinkedin.com/in/letimle

@letimle

A song recommendation engine.Data feed from hypem.com

songprof.fr:Proof-of-concept recommendation enginehypem.com user data

HypeMachine does not make recommendations. Vs. competition / lost engagement

Users ♥ songs they like

Blog posts aggregated into play-list

Hype Machine: “The best place to �nd new music on the web. ”

The data:

125,566 Songs

9,000 Users

songprof.fr:Proof-of-concept recommendation enginehypem.com user data

900k User + Song Interactions to model

An example of 687 songs with 27 Users in common

(Limited to interactions within last year)

Bipartite Graph relationship

Commonalities in behavior -> song recommendations

Collaborative �ltering

Method:1) Generate features based on nearest neighbor song commonalities, between users2) Use ML to classify quality of recomendations, based on features from 1)

Why use two stage approach?Network properties alone not su�cient for predicting if song liked.

Optimal network features need to be discovered.

Song

UserUser

♥User

User

♥♥

Song

Song

Song♥

Possible Recommendation

User

UserNumber of users that both liked a songTotal number possible likes

1) Generate features based on nearest neighbor song commonalities, between users

Advantages:- Very e�cient- Features can be used with ML

Disadvantages:- Su�ers from cold start problem (vs. more complex, cpu expensive al-

Song

UserUser

♥User

User

♥♥

Song

Song

Song♥

Possible Recommendation

User

User

Training/test sets: 67k/18k songs

♥ = Number of users that both liked a song Total number possible likes

Gradient Boosting Machine (GBM) Classi�er. 6 fold CV.Did user like song, yes/no?

78 % Raw accuracy (liked yes/no)64% Predictive accuracy (AUC)

Out of sample validation of model:

2) Use ML to classify quality of recomendations, based on network features

For subset of data, generate predictions, check against historical ♥ data:

Demo: www.songprof.fr

Songs ordered by likelihood of being liked, Given source song being liked

Based on network similarity metricsFed into model

Trained on actual ‘likes’ behavior

Thanks!