ABSTRACT:
With the rapid growth of networking, cyber_physical_social systems (CPSSs) provide vast amounts
of information. Aimed at the huge and complex data provided
by networking, obtaining valuable information to meet precise search needs when
capturing user intention has become a major challenge, especially in
personalized websites. General search engines face difficulties in
addressing the challenges brought by this exploding amount of information. In
this paper, we use real-time location and relevant feedback technology to
design and implement an efficient, configurable, and intelligent retrieval
framework for personalized websites in CPSSs. To improve the retrieval results,
this paper also proposes a strategy of implicit relevant feedback based on
click-through data analysis, which can obtain the relationship between the user
query conditions and retrieval results. Finally, this paper designs a
personalized PageRank algorithm including modified
parameters to improve the ranking quality of the retrieval results using the
relevant feedback from other users in the interest group. Experiments
illustrate that the proposed accurate and intelligent retrieval framework
improves the user experience.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
YAYUAN TANG, HAO
WANG, KEHUA GUO, YIZHE XIAO, AND TAO CHI, “Relevant Feedback Based Accurate and
Intelligent Retrieval on Capturing User Intention for Personalized Websites”,
IEEE Access,2018.