Issues and Challenges of User Intent Discovery (UID) during Web Search

Автор: Wael K. Hanna, Aziza S. Aseem, M. B. Senousy

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 7 Vol. 7, 2015 года.

Бесплатный доступ

There is a need to a small set of words –known as a query– to searching for information. Despite the existence gap between a user’s information need and the way in which such need is represented. Information retrieval system should be able to analyze a given query and present the appropriate web resources that best meet the user’s needs. In order to improve the quality of web search results, while increasing the user’s satisfaction, this paper presents the current work to identify user’s intent sources and how to understand the user behavior and how to discover the users’ intentions during the web search. This paper also discusses the social network analysis and the web queries analysis. The objective of this paper is to present the challenges and new research trends in understanding the user behavior and discovering the user intent to improve the quality of search engine results and to search the web quickly and thoroughly.

Еще

Query, Information Retrieval, Web Search, Social Networks, User Behavior and User Intent

Короткий адрес: https://sciup.org/15012324

IDR: 15012324

Список литературы Issues and Challenges of User Intent Discovery (UID) during Web Search

  • Cristina Gonz´alez-Caro, Ricardo Baeza-Yates: Supervised Identification of the User Intent of Web Search Queries, Department of Information and Communication Technologies, 2011.
  • Liliana Calderón-Benavides, Ricardo BaezaYates: Unsupervised Identification of the User’s Query Intent in Web Search, Department of Information and Communication Technologies 2011.
  • Bing Liu: Web Data Exploring Hyperlinks, Contents, and Usage Data , Springer-Verlag, Berlin Heidelberg New York, 2 ed, 2011.
  • Martin Atzmueller: Social Behavior in Mobile Social Networks: Characterizing Links, Roles, and Communities, Daqing Zhang, Zhiyong Yu, Bin Guo, and Zhu Wang, Mobile Social Networking Computational Social Sciences, Exploiting Personal and Community Context in Mobile Social Networks, Mobile Social Networking Computational Social Sciences, Springer-Verlag, Berlin Heidelberg New York PP. 65-78, PP.109-138, 2014.
  • Dirk Lewandowski: Web Search Engine Research. Wiestaw Pietruszkiewicz and Kerstin Denecke, The Computational Analysis of Web Search Statistics in the Intelligent Framework Supporting Decision Making and Diversity-Aware Search: New Possibilities and Challenges for Web Search, Library and Information Science, Hamburg University of Applied Sciences, Germany, PP. 79–102, PP. 139–162,2013.
  • M.B.Senousy, Wael Karam,: A Comparative Study for Internet Search Engines and Web Crawlers”, SAMS, Cairo, Egypt, 2011.
  • M.B.Senousy, Wael Karam: Investigation of free open source Search Engines”, In Proceedings of the Conference on Computer Science and Software Techniques, Czech Republic, pp.144-168, 2011.
  • Debora Donato, Pinar Donmez, and Sunil: Toward a deeper understanding of user intent and query expressiveness: Yahoo! Lab, USA, 2011.
  • Dirk Lewandowski, Jessica Drechsler, and Sonja von Mach: Deriving Query Intent from Web Search Engine Queries, in Journal of the American Society for Information Science and Technology, 2012.
  • Alejandro Figueroa, Gunter Neumann: Exploiting User Search Sessions for the Semantic Categorization of Question-like Informational Search Queries, In Proceedings of the Sixth International Joint Conference on Natural Language Processing, Japan, PP. 902–906, 2013.
  • Pengjie Ren, Zhumin Chen, Xiaomeng Song, Bin Li, Haopeng Yang, and Jun Ma: Understanding Temporal Intent of User Query Based on Time-Based Query Classification, Communications in Computer and Information Science, Vol.400. Springer-Verlag, Berlin Heidelberg New York, PP. 334-345, 2013.
  • Asli Celikyilmaz, Dilek Hakkani Tur and Gokhan T¨ur: Leveraging Web Query Logs to Learn User Intent Via Bayesian Discrete Latent Variable Mode, , In Proceedings of the of the 28th International Conference on Machine Learning, USA, 2011.
  • Cristina González-Caro, Mari-Carmen Marcos: Different Users and Intent: An Eye-tracking Analysis of Web Search, Pompeu Fabra University, Spain, 2011.
  • O. Chapelle, S. Ji, C. Liao, E. Velipasaoglu, L. Lai and S.-L. Wu: Intent-based Diversification of Web Search Results: Metrics and Algorithms, Yahoo! Labs, Microsoft Bing, CA, 2011.
  • Botao Hu, Yuchen Zhang, Weizhu Chen, Gang Wang and Qiang Yang : Characterizing Search Intent Diversity into Click Models, In Proceeding of the International ACM World Wide Web Conference Committee (IW3C2), India, 2011.
  • Yuchen Zhang, Weizhu Chen, Dong Wang, Qiang Yang,: User-click Modeling for Understanding and Predicting Search-behavior, In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, USA, PP. 1388-1396, 2011.
  • Nicolaas Matthijs, Filip Radlinski :Personalizing Web Search using Long Term Browsing History, In Proceedings of the fourth ACM international conference on Web search and data mining, USA, PP. 25-34, 2011.
  • Giorgos Giannopoulos, Timos Sellis: Personalizing Search Results on User Intent, NTU Athens IMIS, Greece, 2012.
  • Chieh-Jen Wang, Yung- Wei Lin, Ming-Feng Tsai, Hsin-Hsi Chen: Mining subtopics from different aspects for diversifying search results, Information Retrieval, Vol.16, No.4. Springer-Verlag, Berlin Heidelberg New York, PP. 452-483, 2012.
  • Vincenzo Deufemia, Massimiliano Giordano, Giuseppe Polese, and Luigi Marco: Exploiting Interaction Features in User Intent Understanding, Web Technologies and Applications Lecture Notes in Computer Sciences, Vol. 7808. Springer-Verlag, Berlin Heidelberg New York, 506-517, 2013.
  • Junjun Wang, Guoyu Tang, Yunqing Xia, Qiang Zhou, Fang Zheng, Qinan Hu, Sen Na and Yaohai Huang :Understanding the Query: THCIB and THUIS at NTCIR-10 Intent Task. In Proceedings of the 10th NTCIR Conference on Evaluation of Information Access Technologies, Tokyo, Japan, 2013.
  • Aymeric Damien, Min Zhang, Yiqun Liu, and Shaoping Ma: Improve Web Search Diversification with Intent Subtopic Mining, Vol. 400. Springer-Verlag, Berlin Heidelberg New York, PP.322-333, 2013.
  • Rodrygo Luis Teodoro Santos: Explicit Web Search Result Diversification, School of Computing Science College of Science and Engineering University of Glasgow, 2013.
  • Yury Ustinovskiy and Pavel Serdyukov: Personalization of Web-search Using Short-term Browsing Context , In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, PP. 1979-1988, 2013.
  • Jinyun Yan, Wei Chu and Ryen W. White: Cohort Modeling for Enhanced Personalized Search, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, PP.1979-1988, 2014.
  • Harshit Kumar , Sungin Lee, Hong-Gee Kim,: Exploiting social bookmarking services to build clustered user interest profile for personalized search, International Journal of Information Sciences, Vol. 281, PP. 399–417, 2014.
  • Nicholas D.Lane, Ye Xu, Hong Lu, Andrew T. Campbell, Tanzeem Choudhury, and Shane B. Eisenman,: Exploiting Social Networks for Large-Scale Human Behavior Modeling”, Pervasive Computing, IEEE , Vol. 10 , No. 4, PP. 45 – 53, 2011.
  • Nicholas D. Lane :Community-Aware Smartphone Sensing Systems , Internet Computing, IEEE, Vol.16 No.3, PP.60 – 64, 2012.
  • Ming Li , Juanzi Li, Lei Hou, and Hai-Tao Zheng : Personalized Diversity Search Based on User’s Social Relationships, Advanced Data Mining and Applications Lecture Notes in Computer Science, Vol. 7713. Springer-Verlag, Berlin Heidelberg New York, PP.663-674, 2013.
  • Amruta Mantri, Priyanka Nawale, Trupti Pardeshi, Rajeshwary Shisode, Reena Pagare : Profile Based Search Engine, International Journal of Computer Trends and Technology, 2013.
  • Omair Shafiq, Tamer N. Jarada, Panagiotis Karampelas, Reda Alhajj, and Jon G. Rokne: Integrating Online Social Network Analysis in Personalized Web Search, the Influence of Technology on Social Network Analysis and Mining, Lecture Notes in Social Networks, Vol. 6. Springer-Verlag, Berlin Heidelberg New York, PP.589-613, 2013
  • Bin Bi, Milad Shokouhi, Michal Kosinski and Thore Graepel, : Inferring the Demographics of Search Users , In Proceedings of Proceedings of the 22nd international conference on World Wide Web, ACM, PP.131-140, 2013.
  • Tommy H. Nguyen and Boleslaw K. Szymanski : Social Ranking Techniques for the Web. In Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, PP. 49-55, 2013.
  • Fabrizio Silvestri: Mining Query Logs: Turning Search Usage Data into Knowledge, In Journal of Foundations and Trends in Information Retrieval, Vol.4 No.1—2, USA, PP.1-174, 2011.
  • Huizhong Duan, Emre Kıcıman and ChengXiang Zhai, “Click Patterns: An Empirical Representation of Complex Query Intents, UIUC Computer Science, Urbana, Microsoft Research, USA, 2012.
  • Claudio Lucchese, Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri and Gabriele Tolomei, : Discovering Tasks from Search Engine Query Logs, ACM Transactions on Information Systems (TOIS), Vol. 31 No. 3, Article No. 14, 2013.
  • Maksims N. Volkovs : Context Models For Web Search Personalization, 2014.
  • David Vallet and Pablo Castells: Personalized Diversification of Search Results, Universidad Autónoma de Madrid Escuela Politécnica Superior, Departamento de Ingeniería Informática, USA, 2012.
Еще
Статья научная