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Personalised recommendations are often based on the prediction of user classifications.
This chapter discusses methods to design and configure a network to handle and benefit from different user classifications.
This work revisits established user classifications and aims to characterise a historically unspecified user category, the Occasional User (OU).
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Thus, the highest priority user in this LP group first starts a heuristic search for the corresponding subband that responds to (17) by using (20).
In the heterogeneous network, however, the SINR metric can be ineffective in user classification.
The experimental results showed that this model had a better performance in user classification [16].
Hence, we present to utilize the SLNR measurement for user classification.
This will require joint user classification according to their data rate requirements.
This kind of user classification is different from the user classification in homogeneous multicell networks in that both its suffered interference and the interference it caused to other users are taken into account.
These works focus on analyzing the features for the specific classification of users and apply those features to find user classification.
Rizos et al. [9] study the problem of semi-supervised, multi-label user classification of networked data in social networks.
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