Wi-Fi Walkman A wireless handhold that shares and recommend(3)

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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

5. References

[1]M. ?stergren. “Sound Pryer Field Trials: Learning About Adding Value to Driving”,

in the workshop Designing for ubicomp in the wild: Methods for exploring the design of mobile and ubiquitous services, In the proceeding of MUM'2003., 2003.

[2] A. Bassoli, C. Cullinan, J. Moore, and S. Agamanolis. “TunA : a mobile music

experience to foster local interactions(poster)”, in UbiComp 2003 the Fifth International Conference on Ubiquitous Computing, Seattle, 12-15 October 2003.

[3]FreeNet, 592f070879563c1ec5da7192

[4]Gnutella, 592f070879563c1ec5da7192

[5]O. D. Gnawali. “A keyword set search system for peer-to-peer networks”, Master’s

thesis, Massachusetts Institute of Technology, June 2002.

[6]On J. Li, B. Loo, J. Hellerstein, F. Kaashoek, D. Karger, and R. Morris. “On the

feasibility of peer-to-peer web indexing”, In Proc. of the 2nd Int. Workshop on Peer-to-Peer Systems, 2003.

[7]Brian Cooper and Hector Garcia-Molina. “Studying search networks with SIL” In the

preceding of IPTPS, 2003.

[8]Bobby Bhattacharjee, Sudarshan Chawathe, Vijay Gopalakrishnan, Pete Keleher,

Bujor Silaghi. “Efficient peer-to-peer searches using result-catching”, In Proc. of the 2nd Int. Workshop on Peer-to-Peer Systems, 2003.

[9]U. Shardanand, P. Maes, 1995. “Social Information Filtering: Algorithms for

Automating ‘Word of Mouth’ ”, In Proceedings of the Conference on Human Factors in Computing Systems (CHI95), 210-217, Denver, Co, ACM Press.

[10]B. Sarwar, G. Karypis, J. Konstan, J. Riedl, 2001. “Item-based collaborative filtering

recommendation algorithms”, In Proceedings of WWW10 Conference, pages 285-- 295, Hong Kong.

[11]J. Konstan, Bo Miller, D. Maltz, J. Herlocker, L. Gordon, J. Riedl. “GroupLens:

Applying Collaborative Filtering to Usenet News”, Communications of the ACM, 40(3), pp. 77-87, 1997

[12]J. S. Breese, D. Heckerman, and C. Kadie, Empirical analysis of predictive

algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98). G. F. Cooper, and S. Moral, Eds.

Morgan-Kaufmann, San Francisco, Calif., 43-52. 1998.

[13]J. A., Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl,

GroupLens: applying collaborative filtering to suenet news. Commun. ACM 40. 77-

87. 1997.

[14]K. George, Evaluation of item-based top-N recommendation algorithms, Technical

Report #00-046, Dept. of C.S., Univ. Of Minnesota, 1999.

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