Predicting implicit search behaviors usinglog analysis

  • Authors

    • L LeemaPriyadharshini
    • S Florence
    • K Prema
    • C Shyamala Kumari
    2018-02-05
    https://doi.org/10.14419/ijet.v7i1.7.9582
  • Search Engine, Search Behavior, Tasks, Ranking, Implicit Behavior.

  • Search engines provide ranked information based on the query given by the user. Understanding user search behavior is an important task for satisfaction of the users with the needed information. Understanding user search behaviors and recommending more information or more sites to the user is an emerging task. The work is based on the queries given by the user, the amount of time the user spending on the particular page, the number of clicks done by the user particular URL. These details will be available in the dataset of web search log. The web search log is nothing but the log which contains the user searching activities and other details like machine ID, browser ID, timestamp, query given by the user, URL accessed etc., four things considered as the important: 1) Extraction of tasks from the sequence of queries given by the user 2) suggesting some similar query to the user 3) ranking URLs based on the implicit user behaviors 4) increasing web page utilities based on the implicit behaviors. For increasing the web page utility and ranking the URLs predicting implicit user behavior is a needed task. For each of these four things designing and implementation of some algorithms and techniques are needed to increase the efficiency and effectiveness.

  • References

    1. [1] Heasoo Hwang, Hady W. Lauw, and Alexandros Ntoulas," Organizing User Search Histories", in IEEE Trans. Knowl. and Data Engg., vol. 24, no. 5, pp. 912-925, 2012.https://doi.org/10.1109/TKDE.2010.251.

      [2] Zhicheng Dou, Ruihua Song, Ji-Rong Wen, and Xiaojie Yuan, “Evaluating the Effectiveness of Personalized Web Search", in IEEE Trans. Knowl. and Data Engg., Vol. 21, No. 8, pp. 1178-1190, 2009.https://doi.org/10.1109/TKDE.2008.172.

      [3] Yang Cao and Guoliang Li, “A User-Friendly Patent Search Paradigm", in IEEE Trans. Knowl. and Data Engg., Vol. 25, No. 6, pp. 1439-1443, 2013.https://doi.org/10.1109/TKDE.2012.63.

      [4] Yongdong Zhang, Senior Member, IEEE, Xiaopeng Yang, and Tao Mei, Senior Member, IEEE, " Image Search Reranking With Query Dependent Click-Based Relevance Feedback", in IEEE Trans. Image Processing, Vol. 23, No. 10, pp. 4448-4459, 2014.https://doi.org/10.1109/TIP.2014.2346991.

      [5] Christoph Kofler, Linjun Yang, Member, IEEE, Martha Larson, Member, IEEE, TaoMei, Senior Member, IEEE, Alan Hanjalic, Senior Member, IEEE, and Shipeng Li, Fellow, IEEE, " Predicting Failing Queries in Video Search", in IEEETrans. Multimedia, Vol. 16, No. 7, pp. 1973-1985, 2014.https://doi.org/10.1109/TMM.2014.2347937.

      [6] John B. Killoran," How to Use Search Engine Optimization Techniques to Increase Website Visibility", in IEEE transactions on professional communication, vol. 56, no. 1, pp. 50-66, 2013.https://doi.org/10.1109/TPC.2012.2237255.

      [7] Chen Gong, Keren Fu, Artur Loza, Qiang Wu, Member, IEEE, Jia Liu, and Jie Yang, " PageRank Tracker: From Ranking to Tracking", in IEEE Trans. Cybernetics, Vol. 44, No. 6, pp. 882-893, 2014.https://doi.org/10.1109/TCYB.2013.2274516.

      [8] A.K. Shanna, Neha Aggarwal, Neelam Duhan and Ranjna Gupta, " Web Search Result Optimization by Mining the Search Engine Query Logs", in IEEE 2010 Int. Conf. Meth. Models in Computer Science, pp. 39-45, 2010.

      [9] Kinam Park, Taemin Lee, Soonyoung Jung, Sangyep Nam, “Extracting Search Intentions from Web Search Logs", in IEEE, 2010.

      [10] Jinjia Cheng, Chuanchang Liu, Yong Peng, “Expression Of User Personalized Search Behavior Based On Keyword Query Series And Bayesian Network",in IEEE, 2009.

      [11] Omair Shafiq, Reda Alhajj, Jon G. Rokne, "Reducing Search Space for Web Service Ranking using Semantic Logs and Semantic FP-Tree based Association Rule Mining", in Proc. IEEE 9th Int. Conf. Semantic Computing, 2015.https://doi.org/10.1109/ICOSC.2015.7050771.

      [12] Xueqing Gong, Xinyu Guo, Rong Zhang, Xiaofeng Heand Aoying Zhou, " Search Behavior Based Latent Semantic User Segmentation for Advertising Targeting", in IEEE 13th Int. Conf. Data Mining, 2013.https://doi.org/10.1109/ICDM.2013.62.

      [13] Athanasios Papagelis and Christos Zaroliagis, Member, IEEE, “A Collaborative Decentralized Approach to Web Search", in IEEE Trans. Sys., Man, and Cybernetics, Vol. 42, No. 5, pp. 1271-1290, 2012.https://doi.org/10.1109/TSMCA.2012.2187887.

      [14] DilekHakkani-T¨ur ,Gokhan Tur ,Larry Heck Asli, Celikyilmaz, Ashley Fidler Dustin Hillard ,Rukmini Iyer, Sarangarajan Parthasarathy, " Employing Web Search Query Click Logs for Multi-Domain Spoken Language Understanding", in IEEE, 2011.

      [15] Shaoming CHEN, Yajun DU, Qiangqiang PENG, “Extracting query expansion terms based on user’s search behavior", in Sec. Int. Symp. Computational Intel. and Design, IEEE computer society, 2009.

      [16] IsakTaksa, Sarah Zelikovitz, Amanda Spink, “Using Web Search Logs to Identify Query Classification Terms", in Int. Conf. Infor. Tech. IEEE, 2007. https://doi.org/10.1109/ITNG.2007.202.

      [17] Ricardo Baeza-Yates, Carlos Hurtado, Marcelo Mendoza and Georges Dupret, " Modeling User Search Behavior", in Proc. of the Third Latin American Web CongressIEEE, 2011.

      [18] Ryen W. White, Mikhail Bilenko, SilviuCucerzan,â€Studying the Use of Popular Destinations to Enhance Web Search Interaction", in Proc. ACM SIGIR, 2007.https://doi.org/10.1145/1277741.1277771.

      [19] Chien-Kang Huang, Lee-Feng Chien, Yen-Jen Oyang, " Relevant Term Suggestion in Interactive Web Search Based on Contextual Information in Query Session Logs", in Journal American Soc. For Infor. Sci. And Tech., 54(7):638–649, 2003.

      [20] Rosie Jones, Benjamin Rey and Omid Madani, Wiley Greiner, "Generating Query Substitutions", in ACM, 2005.

      [21] Craig Siverstein, Hannes marais, Monika Henzinger and Michael moraiz, "Analysis of very large web search engine query logs" , in Compaq System Research, 2000.

      [22] Yufei Tao, "Fast nearest Neighbor Search with Keywords", in IEEE Trans. Knowl. And Data Engg. Vol 25, 2014.

      [23] Chao Li, Bin Wu, “The Analysis of Youths’ Searching Behavior", in Int. Journal of Comp. And Comm. Engg, 2011.

      [24] GU Hong, ZHAO Guangzhou, "Online Metric Learning for Relevance Feedback in E-Commerce Image Retrieval Applications", in Tsinghua Science And Tech.,Vol 16, No 4, 2011.

      [25] Mark Sanderson and W. Bruce Croft," The History of Information Retrieval Researchâ€, in Proc. IEEE, Vol. 100, 2012.

      [26] Kai Li, "Running and Chasing - The Competition between Paid Search Marketing and Search Engine Optimization", in 47th Hawaii Int. Conf. System Science, 2013.

      [27] FabrícioBenevenuto, "Characterizing User Behavior in Online Social Networks", in IMC’09, 2009.

      [28] Adish Singla," Studying Trailfinding Algorithms for Enhanced Web Search", in SIGIR’10, 2010.

      [29] Yang Song, Hao Ma, Hongning Wang, Kuansan Wang," Exploring and Exploiting User Search Behavior on Mobile and Tablet Devices to Improve Search Relevance", in ACM, 2013.

      [30] Laura A. Granka," Eye-Tracking Analysis of User Behavior in WWW Search", in SIGIR ‘04, 2004.

      [31] Zhen Liao, Yang Song, Yalou Huang, Li-wei He, and Qi He, “Task Trail: An Effective Segmentation of User Search Behaviorâ€, in IEEE Trans. Knowl. And Data Engg, Vol. 26, No. 12, pp. 3090-3102, 2014.https://doi.org/10.1109/TKDE.2014.2316794.

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  • How to Cite

    LeemaPriyadharshini, L., Florence, S., Prema, K., & Shyamala Kumari, C. (2018). Predicting implicit search behaviors usinglog analysis. International Journal of Engineering & Technology, 7(1.7), 91-95. https://doi.org/10.14419/ijet.v7i1.7.9582