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

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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!