radio recommender system for fmhost

Online Recommender System for Radio Station Hosting

Vasily Zaharchuk (HSE)Dmitry Ignatov (HSE)

Andrey Konstantinov (HSE)Sergey Nikolenko (SMI RAS)

BIR 2012HSE, Nizhniy Novgorod

Outline

•FMhost Online Radio Hosting• Recommender Model

▫Data▫Model and Algorithms

•Quality of Service Evaluation (QoS)▫User and Radio Station Activity Analaysis▫Evaluation Technique

•Conclusion

Online Radio Hosting FMhost

•FMhost.me or Host.fm•Real radio, not a streamer•Social network•Lives•New features•Listener oriented•Likes•Favorites

Users

•Unauthorized•Listeners•DJs•Station owners

Recommender System

Why is it needed?

The Previous Algorithm

•Ignatov et al. 2011

Math

•Math math math math math math math math

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The Model: Data

•U is a set of users, R is a set of radio stations, T is a set of tags

•A=(aut), B=(brt), and C=(cur)• frequency vectors

• Normalized matrices, e.g.

The Model

The Model: Importance Weights•Edwards, W. & Barron, F. (1994)

Individual-Based RS (Algorithm RecBi3.1)•RecBi3.1 uses Af and Bf

•For a particular user

• Rank function ,

Collaborative-Based RS (Algorithm RecBi3.2)

•RecBi3.2 uses Cf and vector nC, the latter contains a total number of listened stations for each

• D is a distance matrix

• Top-k neighbors

Collaborative-Based RS (Algorithm RecBi3.2)

•The set of listened stations

• Top-N recommendations

End Recommender System (RecBi3.3)

• Final ranking

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QoS: Distribution Analsysis

•Looking for Power Law P(x)=Cx-a

QoS: Distribution Analysis

QoS: Distribution Analysis

•Pareto Principle (20%:80%)

•50%:80% for radio stations•50%:83% for user visits

QoS assessment

IBRS and CBRS

QoS assessment

IBRS and CBRS

ERS

Conclusion

•Validation on new datasets

•Scalability issues

•Folksonomic nature of data &

Triclustering

Q&A

Thank you!