تأثیر الگوی موضوعی رفتار جستجوی كاربران نوجوان بر پیشنهاد پرس‌وجو
محورهای موضوعی : مهندسی برق و کامپیوتر
حیدر قاسم‌زاده
1
,
محمد قاسم زاده
2
,
علي‌محمد زارع بيدكي
3
1 - دانشگاه یزد
2 - مهندسی کامپیوتر
3 - دانشگاه يزد
کلید واژه: الگوی موضوعیپیشنهاد پرس‌وجورفتار جستجوکاربر نوجوانلاگ جستجو,
چکیده مقاله :
کاربران نوجوان هنگام جستجوی موضوع‌های مورد نظرشان، دایره لغات محدودی را در فرمول‌بندی پرس‌وجو به کار می‌برند. مسئله مهم دیگر آن است که کاربران نوجوان غالباً بر روی اقلام اولیه ارائه‌شده در لیست نتایج جستجو کلیک می‌کنند. در این پژوهش برای ترمیم و جبران این ویژگی‌ها، پیشنهاد می‌شود که الگوی موضوعی از روی رفتار کاربر نوجوان بر اساس جستجوهای قبلی کشف شوند و با تکیه بر الگوهای یافت‌شده، پرس‌وجوی مناسب استخراج و به کاربر نوجوان پیشنهاد گردد. در روش پیشنهادی، الگوهای موضوعی بر اساس ویژگی محبوبیت كلیك‌ها و مرتبط‌ترین موضوع‌ها از روی لاگ‌های جستجو که عموماً حجیم هستند استخراج می‌گردند. در ادامه با استفاده از كلاسه‌بندی دودویی، نزدیک‌ترین پرس‌وجو به پرس‌وجوی مورد نظر كاربر نوجوان مشخص می‌شود. در نتیجه با فیلترنمودن نویز ناوبری موضوعی بر اساس استخراج الگوهای موضوعی کلیک‌های کاربران نوجوان یک مدل کاربر با دقت بالاتری برای پیشنهاد پرس‌وجو حاصل می‌گردد. روش پیشنهادی با استفاده از ابزارهای Alteryx و weka پیاده‌سازی و عملکرد آن بر روی لاگ جستجوی AOL که شامل حدود 20 ميليون نمونه تراکنش جستجو مربوط به 650 هزار کاربر می‌باشد ارزیابی گردید. نتایج آزمایش‌ها نشان می‌دهند که پرس‌وجوهای ارائه‌شده توسط سیستم پیشنهادی به پرس‌وجوی مورد نظر کاربر نوجوان نزدیک‌تر است و به تبع آن موجب بهبود دستیابی به نتایج مرتبط می‌گردد.
Teenager users apply a limited vocabulary when they proceed to look for their desired materials. Another important issue is that teenagers often click mostly on the first items presented in the list of the search results. This research shows that, in order to amend and compensate these issues, we can extract and suggest a more appropriate query to the teenager user. This can be accomplished by discovering the relevant subject patterns from the behavior of the teenage user according to his or her previous search quarries and based on the already found patterns. In the proposed method, the topic patterns of the user are discovered based on the popularity of the clicks and the most relevant topics from the search logs which are generally massive. Afterwards, by using the binary classification method, the closest query to the query given by the user would be specified. Then, by filtering the subject navigation noise via extraction of the subject patterns of the teen user’s clicks, a user model with a higher accuracy can be obtained. We evaluated performance of the proposed method using the Alteryx and Weka tools, over the AOL search log, which includes about twenty million sample search transactions from six hundred and fifty different users. The results obtained from the experiments indicate that the queries presented by the proposed system are closer to the target user's query, and consequently, leads to achievement of more related results.
[1] A. T. Mulik and H. Palkar, "A survey on development of search engine," Int. Advanced Research J. in Science, Engineering and Technology, vol. 4, no. 4, pp. 116-117, Jan. 2017.
[2] M. Madden, A. Lenhart, M. Duggan, S. Cortesi, and U. Gasser, Teens and Technology 2013, Washington, DC: Pew Research Center's Internet & American Life Project, 2013.
[3] A. Druin, E. Foss, H. Hutchinson, E. Golub, and L. Hatley, "Children's roles using keyword search interfaces at home," in Proc. of the 28th Int. Conf. on Human Factors in Computing Systems-CHI'10, pp. 413-422, Atlanta, GA, USA, 10-15 Apr. 2010.
[4] D. Bilal, "Children's use of the Yahooligans! web search engine. III. cognitive and physical behaviors on fully self-generated search tasks," J. of the American Society for Information Science and Technology, vol. 53, no. 13, pp. 1170-1183, Nov. 2002.
[5] D. Bilal, "Children's use of the Yahooligans! web search engine: II. cognitive, physical, and affective behaviors on fact-based search tasks," J. of the American Society for Information Science and Technology, vol. 52, no. 2, pp. 118-136, Oct. 2001.
[6] M. Caramia, G. Felici, and A. Pezzoli, "Improving search results with data mining in a thematic search engine," Computers and Operations Research, vol. 31, no. 14, pp. 2387-2404, Dec. 2004.
[7] R. Baeza-Yates, C. Hurtado, and M. Mendoza, "Query recommendation using query logs in search engines," in Proc. of the Int. Conf. on Current Trends in Database Technology, EDBT'04, pp. 588-596, Heraklion, Greece, 14-18 Mar. 2004.
[8] E. Foss, et al., "Children's search roles at home: implications for designers, researchers, educators, and parents," J. of the American Society for Information Science and Technology, vol. 63, no. 3, pp. 558-573, Mar. 2012.
[9] M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, "Mining data streams: a review," SIGMOD Rec., vol. 34, no. 2, pp. 18-26, Jun. 2005.
[10] 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, pp. 13847-13856, Oct. 2011.
[11] J. Wen, J. Nie, and H. Zhang, "Clustering user queries of a search engine," in Proc. 10th Int. Conf. on World Wide Web, WWW'01, pp. 162-168, Hong Kong, China, 1-5 May. 2001.
[12] 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, pp. 6-12, Fall 1999.
[13] A. Spink, D. Wolfram, M. B. J. Jansen, and T. Saracevic, "Searching the web: the public and their queries," J. of the American Society for Information Science and Technology, vol. 52, no. 3, pp. 226-234, Feb. 2001.
[14] G. Pass, A. Chowdhury, and C. Torgeson, "A picture of search," in Proc. of the 1st Int. Conf. on Scalable Information Systems, InfoScale'06, vol. 152, 7 pp., Hong Kong, 30 May- 1 Jun. 2006.
[15] D. J. Brenes and D. Gayo-Avello, "Stratified analysis of AOL query log," Information Sciences, vol. 179, no. 12, pp. 1844-1858, May 2009.
[16] R. Jones and K. L. Klinkner, "Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs," in Proc. of the 17th ACM Conf. on Information and Knowledge Management, pp. 699-708, Napa Valley, CA, USA, 26-30 Oct. 2008.
[17] R. Kumar and A. Tomkins, "A characterization of online browsing behavior," in Proc. of the 19th Int. Conf. on World Wide Web, WWW'10, pp. 561-570, Raleigh, NC, USA, 26-30 Apr. 2010.
[18] Z. Cheng, B. Gao, and T. Liu, "Actively predicting diverse search intent from user browsing behaviors," in Proc. of the 19th Int. Conf. on World Wide Web, WWW'10, pp. 221-230, Raleigh, NC, USA, 26-30 Apr. 2010.
[19] S. D. Torres, D. Hiemstra, I. Weber, and P. Serdyukov, "Query recommendation for children," in Proc. of the 21th ACM Int. Conf. on Information and Knowledge Management, CIKM'12, pp. 2010-2014, Maui, HI, USA, 29 Oct. 2- Nov. 2012.
[20] S. D. Torres, D. Hiemstra, and T. Huibers, "Vertical selection in the information domain of children," in Proc. of the 13th ACM/IEEE-CS Joint Conf. on Digital Libraries, JCDL'13, pp. 57-66, 22-26 Jul. 2013.
[21] S. D. Torres, D. Hiemstra, I. Weber, and P. Serdyukov, "Query recommendation in the information domain of children," J. of the Association for Information Science and Technology, vol. 65, no. 7, pp. 1368-1384, Jul. 2014.
[22] Y. Wang and E. Agichtein, "Query ambiguity revisited: clickthrough measures for distinguishing informational and ambiguous queries," in the Annual Conf. of the North American Chapter of the Association for Computational Linguistics, pp. 361-364, 2-4 Jun. 2010.
[23] H. Duan, E. Kiciman, and C. Zhai, "Click patterns: an empirical representation of complex query intents," in Proc. of the 21st ACM Int. Conf. on Information and Knowledge Management, CIKM'12, pp. 1035-1044, Maui, HI, USA, 29 Oct.- 2 Nov. 2012.
[24] D. Beeferman and A. Berger, "Agglomerative clustering of a search engine query log," in Proc. of the 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD'00, pp. 407-416, Boston, MA, USA, 20-23 Aug. 2000.
[25] M. Hosseini and H. Abolhassani, "Clustering search engine log for query recommendation," in Proc.-Advances in Computer Science and Engineering, vol. 6, pp. 380-387, Kish Island, Iran, 9-11 Mar. 2008.
[26] Alteyx Inc., "Ateryx Designer x64," Boulder, Colorado, 2015. Availabe: http://www.alteryx.com.
[27] "Weka 3: Data Mining Software in Java," Machine Learning Group at the University of Waikato, Hamilton, New Zealand, 2015. Availabe: http://www.cs.waikato.ac.nz/~ml/weka/.
[28] R. Wetzker, C. Zimmermann, and C. Bauckhage, "Analyzing social bookmarking systems: a del.icio.us cookbook," in Proc. of the ECAI Mining Social Data Workshop, pp. 26-30, Jul. 2008.
