Browse > Article
http://dx.doi.org/10.5392/JKCA.2013.13.01.019

Improving Performance of Search Engine Using Category based Evaluation  

Kim, Hyung-Il (나사렛대학교 멀티미디어학과)
Yoon, Hyun-Nim (한국폴리텍대학 안성여자캠퍼스 디지털정보과)
Publication Information
The Journal of the Korea Contents Association / v.13, no.1, 2013 , pp. 19-29 More about this Journal
Abstract
In the current Internet environment where there is high space complexity of information, search engines aim to provide accurate information that users want. But content-based method adopted by most of search engines cannot be used as an effective tool in the current Internet environment. As content-based method gives different weights to each web page using morphological characteristics of vocabulary, the method has its drawbacks of not being effective in distinguishing each web page. To resolve this problem and provide useful information to the users, this paper proposes an evaluation method based on categories. Category-based evaluation method is to extend query to semantic relations and measure the similarity to web pages. In applying weighting to web pages, category-based evaluation method utilizes user response to web page retrieval and categories of query and thus better distinguish web pages. The method proposed in this paper has the advantage of being able to effectively provide the information users want through search engines and the utility of category-based evaluation technique has been confirmed through various experiments.
Keywords
Information System; Information Retrieval; Information Filtering; Information Category;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. Kaul, Y. Yun, and S. Kim, "Ranking billions of web pages using diodes," Communications of the ACM, Vol.52, No.8, pp.132-136, 2009.
2 F. Liu, C. Yu, and W. Meng, "Personalized Web Search for Improving Retrieval Effectiveness," IEEE Transactions on Knowledge and Data Engineering, Vol.16, No.1, pp.28-40, 2004.   DOI   ScienceOn
3 J. Zobel and A. Moffat, "Inverted files for text search engines," ACM Computing Surveys, Vol.38, No.2, 2006.
4 K. W. Leung, W. NG, and D. L. Lee, "Personalized Concept-Based Clustering of Search Engine Queries," IEEE Transactions on Knowledge and Data Engineering, Vol.20, No.11, pp.1505-1518, 2008.   DOI   ScienceOn
5 A. N. Langville and C. D. Meyer, Google's PageRank and Beyond: The Science of Search Engine Rankings, Princeton University Press, 2006.
6 J. M. Kleinberg, "Authoritative sources in a hyperlinked environment,"The Journal of the ACM, Vol.46, Issue.5, pp.604-632, 1999.   DOI   ScienceOn
7 T. Chen, W. Han, H. Wang, Y. Zhou, B. Xu, and B. Zang, "Content Recommendation System Based on Private Dynamic User Profile," International Conference on Machine Learning and Cybernetics, pp.2112-2118, 2007.
8 M. N. Uddin, J. Shrestha, and G. Jo, "Enhanced Content-Based Filtering Using Diverse Collaborative Prediction for Movie Recommendation," 2009 First Asian Conference on Intelligent Information and Database Systems, pp.132-137, 2009.
9 D. Billsus and M. Pazzani, "Learning Collaborative Information Filters," Proceedings of the 15th International Conference on Machine Learning, 1998.
10 B. Krulwich, "Lifestyle Finder: Intelligent user profiling using large-scale demographic data," Artificial Intelligence Magazine, Vol.18, No.2, 1997.
11 L. Deng, W. Ng, X. Chai, and D.L. Lee, "Spying Out Accurate User Preferences for Search Engine Adaptation," Advances in Web Mining and Web Usage Analysis, LNCS3932, pp.87-103, 2006.
12 C. Jian, Y. Jian, and H. Jin, "Automatic content-based recommendation in e-commerce," The 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service, pp.748-753, 2005.
13 C. D. Manning and H. Schutze, Foundations of Statistical Natural Language Processing, MIT Press, 1999.
14 J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2001.
15 J. Z. Huang, M. K. Ng, H. Rong, and Z. Li, "Automated variable weighting in k-means type clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.5, pp.657-668, 2005.   DOI   ScienceOn
16 S. H. Al-Harbi, "Adapting k-means for supervised clustering," Applied Intelligence, Vol.24, No.3, pp.219-226, 2006.   DOI   ScienceOn
17 P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, 2006.
18 S. K. Ray and S. Singh, "Blog content based recommendation framework using WordNet and multiple Ontologies," 2010 International Conference on Computer Information Systems and Industrial Management Applications, pp.432-437, 2010.
19 B. J. Jansen and A. Spink, "How are we searching the World Wide Web? A comparison of nine search engine transaction logs," Information Processing & Management, Vol.42, No.1, pp.248-263, 2006.   DOI   ScienceOn
20 G.A. Miller and F. Hristea, "WordNet Nouns: Classes and Instances," Computational Linguistics, Vol.32, No.1, pp.1-3, 2006.   DOI   ScienceOn