Discovering Popular Clicks\\\' Pattern of Teen Users for Query Recommendation


Computer Engineering Department, Yazd University, Yazd, Iran


Search engines are still the most important gates for information search in internet. In this regard, providing the best response in the shortest time possible to the user's request is still desired. Normally, search engines are designed for adults and few policies have been employed considering teen users. Teen users are more biased in clicking the results list than are adult users. This leads to fewer clicks on the lowly-ranked search results. Such behavior reduces teen users’ navigation and result extraction skills. With an increase in information load and in teen’s demands, lack of efficient methods leads to inefficiency of search engines regarding teen users. For the purpose, this study discovers teen users’ search behavior and its application in yielding an improved search is strongly recommended. In this way, the pattern of teen users’ popular clicks is identified from a large search log through mining of users’ search transactions based on the frequency and similarity of the clicks in the search log. Then, using binary classification, the closest query into the teen user’s desired one is identified. To discover teen users’ behavior, we took advantage of the AOL query log. System efficiency was examined on the AOL query search log. Results reveal that click pattern improves approaching the query to the one desired by teen users. Generally, this study can demonstrate that in data recovery, application of click behavior and its binary classification can result in improved access of teen users to their desired results.


  1. T. Raghunadha Reddy, B. Vishnu Vardhan and P. Vijayapal Reddy, "A Document Weighted Approach for Gender and Age Prediction Based on Term Weight Measure," International Journal of Engineering, Transactions B: Applications, Vol. 30, No. 5, (2017), 643-651.
  2. M. Mitsui and C. Shah, "Query Generation as Result Aggregation for Knowledge Representation," Proceedings of the 50th Hawaii International Conference on System Sciences, (2017), 4365-4374.
  3. M. Caramia, G. Felici, and A. Pezzoli, "Improving search results with data mining in a thematic search engine," Computers & Operations Research, Vol. 31, No. 14, (2004), 2387-2404.
  4. R. Baeza-Yates, C. Hurtado, and M. Mendoza, "Query Recommendation Using Query Logs in Search Engines," EDBT'04 Proceedings, (2004), 588-596.
  5. E. Foss et al., "Children’s search roles at home: Implications for designers, researchers, educators, and parents," Journal of the American Society for Information Science and Technology, Vol. 63, No. 3, (2012) , 558-573.
  6. M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, "Mining data streams: a review," SIGMOD, Vol. 34, No. 2, (2005), 18-26.
  7. L. Rutkowski, M. Jaworski, L. Pietruczuk, and P. Duda, "A new method for data stream mining based on the misclassification error," IEEE Trans. IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 5, (2015), 1048-1059.
  8. P. Domingos and G. Hulten, "Mining High-Speed Data Streams," Proceedings of The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2000), 71-80.
  9. Y. Liu, J. Miao, M. Zhang, S. Ma, and L. Ru, "How do users describe their information need: Query recommendation based on snippet click model," Expert Systems with Applications, Vol. 38, No. 11, (2011), 13847-13856.
  10. J. Wen, J. Nie, and H. Zhang, "Clustering user queries of a search engine," In Tenth International World Wide Web Conference (WWW), No. 49, (2001), 162-168.
  11. C. Silverstein, H. Marais, M. Henzinger, and M. Moricz, "Analysis of a very large web search engine query log,"ACM SIGIR Forum, Vol. 33, No. 1, (1999), 6-12.
  12. A. Spink, D. Wolfram, M. B. J. Jansen, and T. Saracevic, "Searching the Web: The Public and Their Queries," Journal of the American Society for Information Science and Technology, Vol. 52, No. 3, (2001), 226-234.
  13. G. Pass, A. Chowdhury, and C. Torgeson, "A picture of search," Proceedings of the 1st international conference on Scalable information systems InfoScale 06, Vol. 152, (2006).
  14. D. J. Brenes and D. Gayo-Avello, "Stratified analysis of AOL query log," Information Sciences, Vol. 179, No. 12, (2009), 1844-1858.
  15. R. Jones and K. L. Klinkner, "Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs," Proceedings of the 17th ACM conference on Information and knowledge management, (2008), 699-708.
  16. R. Kumar and A. Tomkins, "A Characterization of Online Browsing Behavior," Proceedings of the 19th International Conference on World Wide Web, (2010), 561-570.
  17. Z. Cheng, B. Gao, and T. Liu, "Actively predicting diverse search intent from user browsing behaviors," WWW '10: Proceedings of the 19th international conference on World wide web, (2010), 221-230.
  18. D. S. Torres, D. Hiemstra, I. Weber, and P. Serdyukov, "Query recommendation for children," Proceedings of the 21th ACM international conference on Information and knowledge management - CIKM ’12, (2012), 2010-2014.
  19. S. D. Torres, D. Hiemstra, and T. Huibers, "Vertical selection in the information domain of children," JCDL '13 Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, (2013), 57-66.
  20. S. D. Torres, D. Hiemstra, I. Weber, and P. Serdyukov, "Query recommendation in the information domain of children," Journal of the Association for Information Science and Technology, Vol. 65, No. 7, (2014), 1368-1384.
  21. Y. Wang and E. Agichtein, "Query Ambiguity Revisited : Clickthrough Measures for Distinguishing Informational and Ambiguous Queries," the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, (2010), 361-364.
  22. H. Duan, E. Kiciman, and C. Zhai, "Click patterns: An Empirical Representation of Complex Query Intents," Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM' 12, (2012),1035-1044.
  23. Z. Markov and D. T. Larose, "Data mining the Web, Uncovering patterns in Web content,structure, and usage," John Wiley & sons Inc., (2007), 115-132.
  24. X. Amatriain, A. Jaimes, N. Oliver, and J. Pujol, "Data mining methods for recommender systems," Recommender Systems Handbook,Springer, (2011), 39–71.
  25. M. Grcar, B. Fortuna, and D. Mladenić, "KNN Versus SVM in the Collaborative Filtering Framework," Learning, (2005), 5-9.
  26. D. Beeferman and A. Berger, "Agglomerative clustering of a search engine query log," Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00, (2000), 407-416.
  27. M. Hosseini and H. Abolhassani, "Clustering Search Engine Log for Query Recommendation," Proceedings-Advances in Computer Science and Engineering, (2008), 380-387.
  28. J. Davis and M. Goadrich, "The relationship between Precision-Recall and ROC curves," in Proceedings of the 23rd international conference on Machine learning-ICML’06, (2006), 233-240.