Sending Play-list
recommended Play-list
Peer
Fig. 4 Recommendation in the client/server model.
The recommendation is implemented in the server part. We utilize a dataset in the AudioScrobbler1 community as our play-list dataset. Currently this dataset has 857.020 1 592f070879563c1ec5da7192/
tracks and 4.175.146 playback actions. The interaction between each peer and the server is illustrated in Fig. 4
Snap-shots of the Wi-Fi walkman application are shown in Fig. 6. The procedure to obtain the suitable music files to fit the user’s interest is illustrated in Fig. 5 and each step is described as follows:
Wi-Fi_Walkman()
Begin
V t to represent the user’s current interest from the play-list by
1.Create ()
q
utilizing a time window.
V t to the recommendation server.
2.Send ()
q
3.Get recommendation from server
4.Finding online peers and obtain the music item list from those peers
5.Select music items from the item list according to the recommendation
6.Locate the recommended items and download/stream them
7.Playback the obtained items
End.
4. Conclusions
In this paper, we introduce a new wireless application called Wi-Fi walkman. In this application, we investigate the technological and usability aspects of human-computer interaction with personalized, intelligent and context-aware wearable devices in ad-hoc wireless environments such as the future home, office, or university campuses.
Without bothering users for any annoying keywords input, the Wi-Fi walkman can steer user’s music interest and recommend appropriate music in the peer-to-peer networks.
In our framework, user’s interest is inferred by the play-list of a user. Based on collaborative filtering methods, system recommends music to users both in the blooding model and the client/server model depending on the local density of the peers.
Figure 5. System Diagram of the Wi-Fi Walkman application
Step. 5Recommended
play-list with the
peers and their
Locations in current ad-
hoc network
current ad-hoc
Fig. 6 Snap-shots of the Wi-Fi Walkman prototype
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