博士 / 國立中央大學 / 資訊工程研究所 / 97 / In order to improve search performance and accuracy, social-like P2P Networks are developed in last years. Our research discover that these methods in social-like P2P Networks can improve search performance in unstructured P2P Networks. However, We find there are three factors that can influence search performance in social-like P2P Networks. First, how to record the peers which have positive response. Second, how to calculate semantic similarity for searching. And third, how to maintain the peer profile. We use Hebbian rule to design the mechanism for calculate the associated weights of peers when they have social interactions, called ‘Associated social-like P2P Networks’. The distinguishing features for improving search performance are the peers with correct responses have higher weights, and adjust the weights by the ability of learning after searching.
| Identifer | oai:union.ndltd.org:TW/097NCU05392090 |
| Date | January 2009 |
| Creators | Lin Jianjun, 林建均 |
| Contributors | Stephen Yang, 楊鎮華 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 33 |
Page generated in 0.0115 seconds