[1]YAN Xinglong,LIU Yiqun,MA Shaoping,et al.Study on website keyword extraction for browsing recommendation[J].CAAI Transactions on Intelligent Systems,2012,7(5):398-403.
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Study on website keyword extraction for browsing recommendation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 5 Page number: 398-403 Column: 学术论文—自然语言处理与理解 Public date: 2012-10-25
Title:
Study on website keyword extraction for browsing recommendation
Author(s):
YAN Xinglong1; 2; 3;  LIU Yiqun1; 2; 3;  MA Shaoping1; 2; 3;  ZHANG Min1; 2; 3;  RU Liyun1; 2; 3
1.Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2.State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
3.Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
browsing recommendation;  keyword recommendation;  keyword extraction;  web keywords
CLC:
TP391
DOI:
-
Abstract:
It is very challenging when conducting research and it is especially difficult as it pertains to website browsing and recommendation system task for selection of suitable keyword usage. This research study will focus on proper use of website browsing and recommendations on how to select keywords for conducting research. The challenge is to leverage user behavior features, as well as develop an effective keyword’s recommendation content page. The implementation of a comprehensive user browsing data, relevant extraction algorithm and algorithm finding methods for new keywords were examined in the research study. The research study also proposed additional, keyword recommendation methods utilizing largescale and related algorithm approaches for domainspecific keyword extraction technology and a query keyword candidate set were compared. The experiment results confirm both methods demonstrate that they satisfy users’ information demand. However, the keyword recommendation methods show a significant performance improvement in effectiveness. The keyword recommendation method has a higher accuracy and better recommendation performance.
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Last Update: 2012-11-13

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