現今大部分P2P網路搜尋軟體缺乏配對興趣相似peer機制，這些P2P系統會造成網路頻寬資源浪費，以及搜尋品質下降。這種現象的問題是由於缺乏相似興趣的peers配對。因此我們發展一套配對相似興趣的peers，以配對方式來分析出相近興趣的peers。篩選結果會根據該peer的興趣配對出極相似高的其它可能提供回答peers列表，同時針對相似高的提供回答的peers進行搜尋資料，而回傳搜尋結果會依照個人興趣喜好的程度來進行分類與排序。為了降低使用者瀏覽搜尋結果時間，系統會將詢問者瀏覽回饋結果來更新使用者興趣喜好程度。在實驗結果得知，我們提出的配對興趣近似peer方法明顯降低使用者搜尋結果時間，在長期興趣下，提升了P2P網路的搜尋效能的準確性，更貼近詢問者資訊需求。 Currently, most of P2P search engines lack a similar interest peer selection mechanism. Hence, it wastes network bandwidth and degenerate the searching quality. Therefore, we develop an interest-based peer selection mechanism. Our approach discovers other similar peers which calculate the interest similarity between a questioner’s preference and other peers’ preference. So, a questioner can obtain other similar peers which are possibility to answer the questioner’s query. Finally, system can transmit the questioner’s query to other similar peers. The query result is classified and ranked by personalized preference to send the questioner. Our method facilitates to satisfy the questioner information requirement and to reduce the searching time. We use a feedback relevance approach to update a peer interest profile. It provides a filtering unnecessary information approach to aim at questioners’ wanting information. In the experiment result, we show that our approach can reduce the searching time. By recording and utilizing long-term user’s interest, we can improve the precision of retrieval performance and satisfy user’s information requirement.
