Open Access Open Access  Restricted Access Subscription or Fee Access

A Hybrid Approach to Personalize Web Search with User Diversity Prediction

Amel Austine, Mathew Kurian

Abstract


It is still an area of research whether personalization is consistently effective on different queries for different users and under different search contexts. Among the different techniques used to improve the web search a click based approach found to be more permissive if the user browse history is available. Here preliminarily a click based approach is implemented in the client side system by storing the client search information in the local system. Then from this information, user’s topical interest pattern is mined and a hybrid approach of both these techniques is implemented. It is also found that most of the personalization techniques apply personalization uniformly irrespective of any consideration. A Click Entropy is used here as a technique to predict the need of personalization. Using a prediction model is also found to be a solution and it is better than using the personalization simply for all queries.

Keywords


Click Entropy, ODP, P-Click, Topical Interest

Full Text:

PDF

References


Zhicheng Dou, Ruihua Song, Ji-Rong Wen, and Xiaojie Yuan, “Evaluating the Effectiveness of Personalized Web Search,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 8, pp. 1178-1190, Aug. 2009.

A. Pretschner and S. Gauch, “Ontology Based Personalized Search,” Proceedings of 11th IEEE Int’l Conf. Tools with Artificial Intelligence (ICTAI ’99), pp. 391-398, Nov. 1999.

P.-A. Chirita, W. Nejdl, R. Paiu, and C. Kohlschu¨ tter, “Using ODP Metadata to Personalize Search,” Proceedings of 28th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’05), pp. 178-185, 2005.

K. Sugiyama, K. Hatano, and M. Yoshikawa, “AdaptiveWebSearch Based on User Profile Constructed without Any Effort from Users,” Proceedings of 13th Int’l World Wide Web Conf. (WWW ’04), pp. 675-684, 2004.

J.-T. Sun, H.-J. Zeng, H. Liu, Y. Lu, and Z. Chen, “CubeSVD:A Novel Approach to Personalized Web Search,” Proceedings of 14th Int’l World Wide Web Conf. (WWW ’05), pp. 382-390,May. 2005.

J. Teevan, S.T. Dumais, and D.J. Liebling, “To Personalize or Not to Personalize: Modeling Queries with Variation in User Intent,” Proceedings of 31th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’08), 2008

Cynthia Dwork, Ravi Kumary, Moni Naorz, and D. Sivakumarx, “Rank Aggregation Methods for the Web,” Proceedings of 10th Int’l World Wide Web Conf. (WWW ’01), pp. 613-622, 2001.

F. Liu, C. Yu, and W. Meng, “Personalized Web Search for Improving Retrieval Effectiveness,” IEEE Transactions on Knowledge and Data Eng., vol. 16, no. 1, pp. 28-40, Jan. 2004.

S. Cronen-Townsend and W.B. Croft, “Quantifying Query Ambiguity,” Proceedings of Second Int’l Conf. Human Language Technology Research (HLT ’02), pp. 94-98, 2002.

Zdravko Markov and Daniel T. Larose, Data Mining the Web Uncovering Patterns in Web Content, Structure, and Usage,3rdEdition, New Jersey:John Wiley & Sons, Inc., Publication,2007,ISBN 978-0-471-66655-4

Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques,2nd Edition, Morgan Kaufmann Publishers, 2006, ISBN 978-1-55860-901-3

X. Shen, B. Tan, and C. Zhai, “Implicit User Modeling for Personalized Search,” Proceedings of ACM Int’l Conf. Information and Knowledge Management (CIKM ’05), pp. 824-831, 2005.

F. Qiu and J. Cho, “Automatic Identification of User Interest for Personalized Search,” Proceedings of 15th Int’l World Wide Web Conference. (WWW ’06), pp. 727-736, 2006.

J. Teevan, S.T. Dumais, and E. Horvitz, “Personalizing Search via Automated Analysis of Interests and Activities,” Proceedings of 28th Ann. Int’l ACM SIGIR Conf. Research and Development inInformation Retrieval (SIGIR ’05), pp. 449-456, 2005

F. Liu, C. Yu, and W. Meng, “Personalized Web Search by Mapping User Queries to Categories,” Proceedings. ACM Int’l Conf. Information and Knowledge Management (CIKM ’02), pp. 558-565, 2002

J.-R. Wen, Z. Dou, and R. Song, “Personalized Web Search,” Encyclopedia of Database Systems, 2009

M. Speretta and S. Gauch, “Personalized Search Based on User Search Histories,” Proceedings of IEEE/WIC/ACM Int’l Conf. Web Intelligence (WI ’05), pp. 622-628, 2005

J.S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proceedings of 14th Conf. Uncertainty in Artificial Intelligence (UAI ’98), pp. 43-52, 1998.

C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, “Rank Aggregation Methods for the Web,” Proceedings of 10th Int’l World Wide Web Conf. (WWW ’01), pp. 613-622, 2001

S. Wedig and O. Madani, “A Large-Scale Analysis of Query Logs for Assessing Personalization Opportunities,” Proceedings of 12th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’06), pp. 742-747, 2006

L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web,” technical report, Computer Science Dept., Stanford Univ., 1998


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.