Radio recommender system for FMHost

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Dmitrii Ignatov

Radio recommender system

Technologie

Online Recommender System
for Radio Station Hosting

Vasily Zaharchuk (HSE) Andrey Konstantinov (HSE)
Dmitry Ignatov (HSE) Sergey Nikolenko (SMI RAS)

BIR 2012
HSE, 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

The Previous Algorithm
• Ignatov et al. 2011

Math
• Math math math math math math math math
• Math math math math math math math math
• Math math math math math math math math
• Math math math math math math math math
• Math math math math math math math math
• Math math math math math math math math
• Math math math math math math math math

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: 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

•

QoS: Distribution Analsysis
• Looking for Power Law P(x)=Cx-

QoS: Distribution Analysis
• Pareto Principle (20%:80%)

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

Conclusion

• Validation on new datasets

• Scalability issues

• Folksonomic nature of data & Triclustering

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