| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 95 === The widespread use of Peer-to-Peer (P2P) systems has made multimedia content sharing more efficient. Users in a P2P network can query and download objects based on their preference for specific types of multimedia content. However, most P2P systems only construct the overlay architecture according to physical network constraints and do not take user preferences into account. In this thesis, we investigate a social-based overlay that can cluster peers that have similar preferences. To construct a semantic social-based overlay, we model a quantifiable measure of similarity between peers so that those with a higher degree of similarity can be connected by shorter paths. Hence, peers can locate objects of interest from their overlay neighbors, i.e., peers who have common interests. In addition, we propose an overlay adaptation algorithm that allows the overlay to adapt to P2P churn and preference changes in a distributed manner. Meanwhile, we introduce methods to handle
multiple types of contents and extended methods to improve the performance of the content searching. We also analysis the number of TTL that is appropriate for the TTL-limited flooding.
We use simulations and a real database called Audioscrobbler,
which tracks users’ listening habits, to evaluate the proposed social-based overlay. The results show that social-based overlay adaptation enables users to locate content of interest with a higher success ratio and with less message overhead. Besides, we compare the performance between the proposed social-based p2p system with the Freenet-type p2p system.
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