On the Design of the Social-based P2P System for Music Recommendation
Date Issued
2008
Date
2008
Author(s)
Kung, Chui-Chiu
Abstract
The pervasive use of Peer-to-Peer (P2P) systems and the growing demand for personalization from the consumers has made future business focus on the niche market instead of the mass market. The recommender system, which is able to timely select interested data to the individual user, has become the key to any successful business.urrently most recommendation systems are based on a centralized architecture, none the less, this is not suitable for P2P networks. The focus of this paper is to propose a distributed system for music search and recommendation on unstructured P2P networks. The idea of our work is to construct a social-based overlay network that can cluster a small set of peers, which have similar tastes for music, from thousands to millions of peers. That is, peers with similar interests can be connected by shorter paths so that they can exchange multimedia content more efficiently. n addition, we choose a set of proper meta-data (a characteristic vector) to represent a music object and use them to construct the characteristic-vector-based content filter. Next, a dominant attribute, which is one of the attributes in the characteristic vector of a music object, is used to build the profile of a peer. With the idea of the social network, a P2P profile-based collaborative filter is proposed. Finally, we explore the item-to-item relationship to construct a history-based cooperative filter. We use simulations and a real database called AudioScrobbler which tracks users'' listening habits, to evaluate the performance of the recommendation system. The results show that our system is able to offer efficient query and significant improvement for recommendation services compared with existing recommendation systems.
Subjects
P2P Network, Social Theory, Recommendation System, Content filter, Collaborative filter, Cooperative filter
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-97-R95922034-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):14817f11e46f09b4d15713cc8d065a03