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Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust


Affiliations
1 Shaheed Rajguru College of Applied Science for Women, University of Delhi, Vasundhra Enclave, Delhi, India
 

The main challenge for effective web Information Retrieval (IR) is to infer the information need from user's query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user's information need. Trust based web page recommendations retrieve search results according to the user's information need. In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user's information need and trust is used for recommendation of web pages according to the user's information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.


Keywords

Search Engines, Intelligent Information Retrieval, Fuzzy Set, Trust, Query Expansion, Web Page Recommendation.
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  • Yu, J.; Liu, F. Mining user context based on interactive computing for personalized Web search. In 2nd International Conference on Computer Engineering and Technology (ICCET),2, 2010; pp. 209-214, IEEE.

  • Pan, X.; Wang, Z.; Gu, X. Context-based adaptive personalized web search for improving information retrieval effectiveness. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing, 2007; pp. 5427-5430, IEEE.

  • Kutub, M.; Prachetaa, R.; Bedekar, M. User Web Search Behaviour. In 3rd International Conference on Emerging Trends in Engineering and Technology (ICETET),2010; pp. 549-554, IEEE.

  • Arzanian B.; Akhlaghian, F.; Moradi, P. A Multi-Agent Based Personalized Meta-Search Engine Using Automatic Fuzzy Concept Networks. In Third International Conference on Knowledge Discovery and Data Mining, 2010; pp. 208-211.

  • Matthijs ,N.; Radlinski, F. Personalizing web search using long term browsing history. In Proceedings of the fourth ACM international conference on Web search and data mining, 2011; pp. 25-34.

  • Xu, S.; Jiang, H.; Lau, F. C. M. Mining user dwell time for personalized web search re-ranking. In In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence, 11,2011; pp. 2367-2372.

  • Chawla, S. Semantic Query Expansion using Cluster Based Domain Ontologies. International Journal of Information Retrieval Research (IJIRR), 2012b; 2(2), pp. 13-28.

  • Chawla, S. Personalized web search using ACO with information scent. International Journal of Knowledge and Web Intelligence, 2013; 4(2), pp. 238-259.

  • Chawla, S. A novel approach of cluster based optimal ranking of clicked URLs using genetic algorithm for effective personalized web search. Applied Soft Computing, 2016; 46, 90-103.

  • Lee, J.; Kim, E. Fuzzy Web Information Retrieval System,2011.

  • Massa, P.; Avesani, P. Trust-aware Recommender Systems. Proceedings of the ACM Conference on Recommender Systems, 2007; pp. 17-24.

  • Levien, R. Attack-resistant Trust Metrics. Ph.D. thesis, 2004, University of California at Berkeley, USA.

  • Lathia N.; Hailes, S ;Capra, L. Trust-based collaborative filtering. Proceedings of the joint iTrust and PST Conference on Privecy, Trust Management and Security. Springer, 2008; pp. 119-134.

  • Hwang C.,S and Chen, Y. P.Using trust in collaborative filtering recommendation. Lecture Notes in Computer Science, 4570, 2007; 1052-1060. Innovation Network (2009).

  • Peng T.; Seng-cho, T. iTrustU: A blog recommender system based on multifaceted trust and collaborative filtering. Proceedings of the ACM Symposium on Applied Computing. New York, NY. 2009; pp. 1278-1285.

  • Massa, P.; Bhattacharjee, B. Using trust in recommender systems: An experimental analysis. Proceedings of the Second International Conference on Trust Management, Oxford, UK., 2004; 221-235, (2004).

  • Jianshu Weng, Chunyan Miao, Angela Goh. Improving Collaborative Filtering with Trustbased Metrics”, SAC’06, April, 2327, Dijon, France, ACM 1595931082/ 06/0004, (2006).

  • Jamali, M. and Ester M. TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation”. Proceedings of the 151th ACM Conference on Knowledge Discovery and Data mining. KDD.09, Paris, France,2009.

  • Chawla, S. Trust in Personalized Web Search based on Clustered Query Sessions. International Journal of Computer Applications, 59(7),2012a; pp. 37-44.

  • Zadeh, LA. The problem of deduction in an environment of imprecision, uncertainty, and partial truth. In: M Nikravesh, B Azvine (eds), FLINT 2001, New Directions in Enhancing the Power of the Internet, UC Berkeley Electronics Research Laboratory, Memorandum No. UCB/ERL M01/28,2001.

  • Beremji , H. Fuzzy reinforcement learning and the internet with applications in power management or wireless networks. In: M Nikravesh, B Azvine (eds), FLINT 2001, New Directions in Enhancing the Power of the Internet, UC Berkeley Electronics Research Laboratory, Memorandum No. UCB/ERL M01/28,2001.

  • Choi, D. Integration of document index with perception index and its application to fuzzy query on the Internet. In: M Nikravesh, B Azvine (eds), FLINT 2001, New Directions in Enhancing the Power of the Internet, UC Berkeley Electronics Research Laboratory, Memorandum No. UCB/ERL M01/28,2001.

  • Rubens, N. O. The application of fuzzy logic to the construction of the ranking function of information retrieval systems., Computer Modelling and New Technologies, 2006; 10(1), pp. 20–27.

  • Mencar, C.; Torsello, M. A.; Dell’Agnello, D.; Castellano, G.; Castiello, C. Modeling user preferences through adaptive fuzzy profiles. In 2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009; pp. 1031-1036, IEEE.

  • Castellano, G.; Dell’Agnello, D.; Fanelli, A. M.; Mencar, C.; Torsello, M. A. A competitive learning strategy for adapting fuzzy user profiles. In 2010 10th International Conference on Intelligent Systems Design and Applications, 2010; pp. 959-964, IEEE.

  • Alzahrani S. M.; Salim, N. On the use of fuzzy information retrieval for gauging similarity of arabic documents. In Second International Conference on the Applications of Digital Information and Web Technologies, 2009; pp. 539-544, IEEE,2009.

  • Chawla, S. Effective Personalization of web search based on Fuzzy Information Retrieval. International Journal of Computer Science and Information Technologies, 2015b; 6 (3) , pp. 2831-2837 28. Van Rijsbergen, C. J. A non-classical logic for information retrieval. The computer journal, 29(6),1986; pp. 481-485.

  • Bordogna, G.; Pasi, G. Modeling vagueness in information retrieval. In Lectures on information retrieval , 2000; 207-241, Springer Berlin Heidelberg.

  • Kraft, D. H.; Bordogna, G.; Pasi, G. Fuzzy set techniques in information retrieval. In Fuzzy sets in approximate reasoning and information systems, 1999; pp. 469-510, Springer US.

  • Miyamoto S. Information retrieval based on fuzzy associations. Fuzzy Sets and Systems, 1990; 38(2), pp.191-205.

  • Zhang, Z.; Nasraoui, O. Mining search engine query logs for query recommendation. In Proceedings of the 15th international conference on World Wide Web,2006; (pp. 1039-1040). ACM.

  • Wang, X.; Bai, Y.; Li, Y. An information retrieval method based on sequential access patterns. In Asia-Pacific Conference on Wearable Computing Systems (APWCS), 2010; pp. 247-250,. IEEE.

  • Eirinaki ,M.; Vazirgiannis, M.;Varlamis, I.SEWeP: using site semantics and a taxonomy to enhance the Web personalization process. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003; pp. 99-108.

  • Liu, X.; Zhou, J. Research on Knowledge-based Personalized Recommendation Service System Retrieval Service. Energy Procedia, 13, 2011; pp. 10103-10108.

  • Kim, S.; Han, S.The method of inferring trust in web-based social network using fuzzy logic”. In international workshop on machine intelligence research, 2009; pp.140-144.

  • Prakash, A.; Mustafi, D. Fuzzy Logic Approach to Combat Web Spam with TrustRank,International Journal of Innovative Research in Computer and Communication Engineering,2013; 1(4), pp. 1100-1106.

  • Kohli, S. Developing and Validating Fuzzy-Based Trust Measures for Online Medical Diagnosis and Symptoms Analysis. Fuzzy Expert Systems for Disease Diagnosis, 302,2014.

  • Folorunso O.and Mustapha, O. A. A fuzzy expert system to Trust-Based Access Control in crowdsourcing environments”. Applied Computing and Informatics, 2015; 11(2), 116-129.

  • Abdul-Rahman A.; Hailes, S. Supporting trust in virtual communities. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000; (pp. 9-pp). IEEE,2000.

  • D Harrison McKnight N. L. C. What trust means in e-commerce customer relationships: an interdisciplinary conceptual typology. International journal of electronic commerce, 6(2), 2002; pp. 35-59.

  • Dimitrakos ,T. A service-oriented trust management framework. In Workshop on Deception, Fraud and Trust in Agent Societies , 2003; pp 53-72, Springer Berlin Heidelberg.

  • O’Donovan, J.; Smyth, B. Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces ,2005; (pp. 167-174). ACM.

  • Pirolli, P. Computational models of information scent-following in a very large browsable text collection , Conference on Human Factors in Computing Systems, 1997; pp. 3-10.

  • Pirolli, P. The use of proximal information scent to forage for distal content on the world wide web, Working with Technology, Mind: Brunswikian. Resources for Cognitive Science and Engineering, Oxford University Press,2004.

  • Chi, E H.; Pirolli, P.; Chen, K.; Pitkow, J. Using Information Scent to model User Information Needs and Actions on the Web, International Conference on Human Factors in Computing Systems, New York, NY, USA, 2001; pp. 490-497.

  • Heer J., and Chi, E.H. Separating the Swarm: Categorization method for user sessions on the web”, International Conference on Human Factor in Computing Systems, 2002; pp. 243-250.

  • Chawla, S.; Bedi, P. Personalized Web Search using Information Scent, International Joint Conferences on Computer, Information and Systems Sciences, and Engineering, Technically Co-Sponsored by: Institute of Electrical & Electronics Engineers (IEEE), University of Bridgeport, published in LNCS (Springer), 2007; pp. 483-488.

  • Chawla, S. ; Bedi ,P. Improving information retrieval precision by finding related queries with similar information need using information scent”. In First International Conference on Emerging Trends in Engineering and Technology, ICETET’08,2008; pp. 486-491, IEEE.

  • Chawla, S. Personalised Web Search using Trust based Hubs and Authorities. International Journal of Engineering Research and Applications, 7, 2014a; pp. 157-170.

  • Chawla, S. Novel Approach to Query Expansion using Genetic Algoirthm on Clustered Query Sessions for Effective Personalized Web Search . International Journal of Advanced Research in Computer Science and Software Engineering, 4(11),2014b; pp 73-81.

  • Chawla, S. Domainwise Web Page Optimization Based On Clustered Query Sessions Using Hybrid Of Trust And ACO For Effective Information Retrieval, International Journal of Scientific and Technology Research, 2015a; 4(11), 196-204.

  • Wen J. R.; Nie, J. Y; Zhang, H. J. Query clustering using user logs. ACM Transactions on Information Systems, 20(1), 2002; pp. 59-81.

  • Zhao, Y.; Karypis, G. Comparison of agglomerative and partitional document clustering algorithms (No. TR-02-014). MINNESOTA UNIV MINNEAPOLIS DEPT OF COMPUTER SCIENCE,2002.

  • Zhao, Y.; Karypis, G. Criterion functions for document clustering: Experiments and analysis,2001.

  • KARN, B. INFORMATION RETRIEVAL SYSTEM USING FUZZY SET THEORY-THE BASIC CONCEPT.

  • Klir, G.; Yuan, B. Fuzzy sets and fuzzy logic , 4, New Jersey: Prentice hall,1995.


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  • Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust

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Authors

Suruchi Chawla
Shaheed Rajguru College of Applied Science for Women, University of Delhi, Vasundhra Enclave, Delhi, India

Abstract


The main challenge for effective web Information Retrieval (IR) is to infer the information need from user's query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user's information need. Trust based web page recommendations retrieve search results according to the user's information need. In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user's information need and trust is used for recommendation of web pages according to the user's information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.


Keywords


Search Engines, Intelligent Information Retrieval, Fuzzy Set, Trust, Query Expansion, Web Page Recommendation.

References





DOI: https://doi.org/10.13005/ojcst%2F10.02.09