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Radio recommender system for FMHost

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Dmitrii Ignatov
Dmitrii Ignatov

Radio recommender system

Tecnología
1 de 21
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
Recommender System Why is it needed?
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
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
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!

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