CFP last date
20 December 2024
Reseach Article

Query based Recommendation and Gaussian Firefly based Clustering Algorithm for Inferring User Feedback Sessions with Search Goals

by P.jayasree, G.suganya, C.daniel Nesa Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 2
Year of Publication: 2015
Authors: P.jayasree, G.suganya, C.daniel Nesa Kumar
10.5120/19949-1758

P.jayasree, G.suganya, C.daniel Nesa Kumar . Query based Recommendation and Gaussian Firefly based Clustering Algorithm for Inferring User Feedback Sessions with Search Goals. International Journal of Computer Applications. 114, 2 ( March 2015), 14-19. DOI=10.5120/19949-1758

@article{ 10.5120/19949-1758,
author = { P.jayasree, G.suganya, C.daniel Nesa Kumar },
title = { Query based Recommendation and Gaussian Firefly based Clustering Algorithm for Inferring User Feedback Sessions with Search Goals },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 2 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number2/19949-1758/ },
doi = { 10.5120/19949-1758 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:53:25.723054+05:30
%A P.jayasree
%A G.suganya
%A C.daniel Nesa Kumar
%T Query based Recommendation and Gaussian Firefly based Clustering Algorithm for Inferring User Feedback Sessions with Search Goals
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 2
%P 14-19
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In web search based applications, queries are suggested by users to search and investigate web search engines information requirements regarding user. However the queries submitted by user sometimes might not easily understood by search engines , since queries submitted by user might be short representation and should not precisely characterize users' detailed information requirements. Because numerous uncertain queries might cover an extensive assortment of topic and diverse users might desire to obtain information on based on their diverse query. Since the query based recommendation and assessment of user search goals based on user feedback sessions might improve retrieval results of web search engine for users. Determining the various number of user search goals for same query and it doesn't automatically discover user search goals based on the user specified query , clustering is performed based on K means clustering. Still K means clustering methods have some major pitfalls such as random selection of initial centroid value and selection of K value; it reduces the grouping results of pseudo documents for user search. In order to shortcoming these issues query based recommendation system and optimization based clustering is proposed in this work. In the proposed work primarily concerns a query based recommendation system to understand user search goals through examining user search engine query logs files. It then creates and suggests the queries with the purpose to cover search goals of user. Second propose a novel Gaussian firefly algorithm (GFA) based clustering method to group similar user pseudo-documents from feedback session which can capably replicate user search goals. Then clustering results of proposed GFA is compared with existing Fuzzy C Means (FCM) and K means clustering methods for web search engines. At end of the clustering method the experimentation results is measured based on the classifier parameters such as Average Precision (AP) Voted Average Precision (VAP) and Classified Average Precision (CAP) to assess the performance accuracy of web search engine results based on the restructured web search results.

References
  1. X. Wang and C. -X Zhai 2007. Learn from Web Search Logs to Organize Search Results. Proc. 30th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '07), pp. 87-94.
  2. H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li 2008 . Context-Aware Query Suggestion by Mining Click-Through. Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (SIGKDD '08), pp. 875-883.
  3. H. -J Zeng, Q. -C He, Z. Chen, W. -Y Ma, and J. Ma 2004 . Learning to Cluster Web Search Results. Proc. 27th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '04), pp. 210-217.
  4. Ardito, C. et al 2005. An approach to usability evaluation of eLearning applications. Universal Access in the Information Society, 4(3): pp. 270-283.
  5. X. S. Yang, Nature-Inspired Metaheuristic Algorithms", Luniver Press, 2008.
  6. H. Chen and S. Dumais 2000. Bringing Order to the Web: Automatically Categorizing Search Results. Proc. SIGCHI Conf. Human Factors in Computing Systems (SIGCHI '00), pp. 145-152.
  7. R. Jones and K. L. Klinkner 2008. Beyond the Session Timeout: Automatic Hierarchical Segmentation of Search Topics in Query Logs. Proc. 17th ACM Conf. Information and Knowledge Management (CIKM '08), pp. 699-708.
  8. A. Spink, B. J. Jansen, and H. C. Ozmultu 2000. Use of query reformulation and relevance feedback by Excite users. Internet Research: Electronic Networking Applications and Policy, 10(4):pp. 317–328.
  9. U. Lee, Z. Liu, and J. Cho 2005 . Automatic Identification of User Goals in Web Search. Proc. 14th Int'l Conf. World Wide Web (WWW '05), pp. 391-400.
  10. R. Baeza-Yates, C. Hurtado, and M. Mendoza 2004. Query Recommendation Using Query Logs in Search Engines. Proc. Int'l Conf. Current Trends in Database Technology (EDBT '04), pp. 588-596.
  11. C. -K Huang, L. -F Chien, and Y. -J Oyang 2003 . Relevant Term Suggestion in Interactive Web Search Based on Contextual Information in Query Session Logs. J. Am. Soc. for Information Science and Technology, vol. 54, no. 7, pp. 638-649.
  12. T. Joachims, L. Granka, B. Pang, H. Hembrooke, and G. Gay 2005 . Accurately Interpreting Clickthrough Data as Implicit Feedback," Proc. 28th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '05) , pp. 154-161.
  13. T. Joachims 2003 . Evaluating Retrieval Performance Using Clickthrough Data," Text Mining, J. Franke, G. Nakhaeizadeh, and I. Renz, eds. , pp. 79-96, Physica/Springer Verlag.
  14. Chen and S. Dumais 2000, "Bringing Order to the Web: Automatically Categorizing Search Results," Proc. SIGCHI Conf. Human Factors in Computing Systems (SIGCHI '00), pp. 145-152.
  15. X. -S. Yang 2010 . Firefly Algorithm, Lévy Flights and Global Optimization. Research and Development in Intelligent Systems XXVI (Eds M. Bramer, R. Ellis, M. Petridis), Springer, pp. 209-218.
Index Terms

Computer Science
Information Sciences

Keywords

User search goals feedback sessions pseudo-documents classified average precision (CAP) Voted AP (VAP) average precision (AP) Information retrieval (IR) Fuzzy C means clustering(FCM) K-means clustering Gaussian Firefly algorithm (GFA).