scholarly journals Search Unstructured Data Performance in P2P Networks Based on User Interest Model

Distributed (P2P) systems build up approximately coupled application-level overlays on high of the web to encourage affordable sharing of assets. They'll be generally delegated either organized or unstructured systems. while not requesting requirements over the topology, unstructured P2P systems is made appallingly speedily and ar so contemplated fitting to the web air. Be that as it may, the arbitrary pursuit strategies received by these systems now and then perform ineffectively with a larger than usual system Size. during this paper, we tend to search for to help the pursuit execution in unstructured P2P arranges through misusing clients' basic intrigue designs caught among a likelihood theoretic structure named the client intrigue model (UIM). an exploration convention and a directing table change convention ar increasingly arranged in order to speed up the hunt technique through self sorting out the P2P organize into somewhat world. Each hypothetical and test examination are Conducted and incontestable the viability and strength of our methodology.

2017 ◽  
Vol 887 ◽  
pp. 012061
Author(s):  
Junkai Yi ◽  
Yacong Zhang ◽  
Mingyong Yin ◽  
Xianghui Zhao

2013 ◽  
Vol 765-767 ◽  
pp. 998-1002
Author(s):  
Shao Xuan Zhang ◽  
Tian Liu

In view of the present personalized ranking of search results user interest model construction difficult, relevant calculation imprecise problems, proposes a combination of user interest model and collaborative recommendation algorithm for personalized ranking method. The method from the user search history, including the submit query, click the relevant webpage information to train users interest model, then using collaborative recommendation algorithm to obtain with common interests and neighbor users, on the basis of these neighbors on the webpage and webpage recommendation level associated with the users to sort the search results. Experimental results show that: the algorithm the average minimum precision than general sorting algorithm was increased by about 0.1, with an increase in the number of neighbors of the user, minimum accuracy increased. Compared with other ranking algorithms, using collaborative recommendation algorithm is helpful for improving webpage with the user interest relevance precision, thereby improving the sorting efficiency, help to improve the search experience of the user.


2019 ◽  
Vol 1237 ◽  
pp. 022067
Author(s):  
Xiaomin Li ◽  
Jianrong Zhang ◽  
Jiabing Wan ◽  
JinKai Zhang ◽  
Chenchao Zhu ◽  
...  

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