Analyzing the Usefulness of the User's Browser History for Generating Query Suggestions
Abstract
A very useful feature of search engines that helps users while they browse the internet, where, they very often, try to satisfy an information need, is query suggestion. This mechanism shows the user a list of possible queries from where he can choose and be able to perform a search easier and faster. In this paper we tried to assess the usefulness of a user's recent web browsing history for generating new query suggestions. We performed a one month experiment in which we collected browsing history logs of several users and searched query terms submitted by those users to Google (using a Chrome plugin) and found that approximately 32% of the queries submitted can be predicted from the user's browsing history.
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[26] https://www.comscore.com/Insights/Rankings/comScore-Releases-February-2016-US-Desktop-Search-Engine-Rankings
[27] https://www.w3schools.com/browsers/
[2] Holger Bast and Ingmar Weber. Type less, find more: Fast autocompletion search with a succinct index. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’06, pages 364-371, New York, NY, USA, 2006. ACM.
[3] Ziv Bar-Yossef and Naama Kraus. Context- sensitive query auto-completion. In Proceedings of the 20th International Conference on World Wide Web, WWW ’11, pages 107-116, New York, NY, USA, 2011. ACM.
[4] Huanhuan Cao, Daxin Jiang, Jian Pei, Qi He, Zhen Liao, Enhong Chen, and Hang Li. Context-aware query suggestion by mining click-through and session data. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’08, pages 875-883, New York, NY, USA, 2008. ACM.
[5] Shengyue Ji, Guoliang Li, Chen Li, and Jianhua Feng. Efficient interactive fuzzy keyword search. In Proceedings of the 18th International Conference on World Wide Web, WWW ’09, pages 371-380, New York, NY, USA, 2009. ACM.
[6] Jyun-Yu Jiang, Yen-Yu Ke, Pao-Yu Chien, and Pu-Jen Cheng. Learning user reformulation behavior for query auto-completion. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’14, pages 445-454, New York, NY, USA, 2014. ACM.
[7] Mario Arias, Jose Manuel Cantera, Jesus Vegas, Pablo de la Fuente, Jorge Cabrero Alonso, Guido Garcia Bernardo, Cesar Llamas, and Alvaro Zubizarreta. Context-based personalization for mobile web search. In PersDB, pages 33-39, Auckland, New Zealand, 2008.
[8] Ji-Rong Wen, Jian-Yun Nie, and Hong-Jiang Zhang. Clustering user queries of a search engine. In Proceedings of the 10th International Conference on World Wide Web, WWW ’01, pages 162-168, New York, NY, USA, 2001. ACM.
[9] Hang Cui, Ji-Rong Wen, Jian-Yun Nie, and Wei-Ying Ma. Probabilistic query expansion using query logs. In Proceedings of the 11th International Conference on World Wide Web, WWW ’02, pages 325-332, New York, NY, USA, 2002. ACM
[10] Holger Bast, Debapriyo Majumdar, and Ingmar Weber. Efficient interactive query expansion with complete search. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM ’07, pages 857-860, New York, NY, USA, 2007. ACM.
[11] Ryen W White and Gary Marchionini. Examining the effectiveness of real-time query expansion. Information Processing and Management, 43(3):685-704, 2007.
[12] Christopher J. C. Burges, Krysta M. Svore, Paul N. Bennett, Andrzej Pastusiak, and Qiang Wu. Learning to rank using an ensemble of lambda-gradient models. In Proceedings of the 2010 International Conference on Yahoo! Learning to Rank Challenge - Volume 14, YLRC’10, pages 25-35. JMLR.org, 2010.
[13] Liangda Li, Hongbo Deng, Anlei Dong, Yi Chang, Hongyuan Zha, and Ricardo Baeza-Yates. Analyzing user’s sequential behavior in query auto-completion via markov processes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’15, pages 123-132, New York, NY, USA, 2015. ACM.
[14] Yanen Li, Anlei Dong, Hongning Wang, Hongbo Deng, Yi Chang, and ChengXiang Zhai. A two-dimensional click model for query auto-completion. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’14, pages 455-464, New York, NY, USA, 2014. ACM.
[15] Bernard J Jansen, Amanda Spink, and Tefko Saracevic. Real life, real users, and real needs: a study and analysis of user queries on the web. Information processing and management, 36(2):207-227, 2000.
[16] Mark Sanderson. Ambiguous queries: Test collections need more sense. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’08, pages 499-506, New York, NY, USA, 2008. ACM.
[17] Paul N. Bennett, Ryen W. White, Wei Chu, Susan T. Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. Modeling the impact of short and long-term behavior on search personalization. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’12, pages 185-194, New York, NY, USA, 2012. ACM.
[18] Nicolaas Matthijs and Filip Radlinski. Personalizing web search using long term browsing history. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, pages 25-34, New York, NY, USA, 2011. ACM.
[19] Mariam Daoud, Lynda Tamine-Lechani, Mohand Boughanem, and Bilal Chebaro. A session based personalized search using an ontological user profile. In Proceedings of the 2009 ACM Symposium on Applied Computing, SAC ’09, pages 1732-1736, New York, NY, USA, 2009. ACM.
[20] Zhicheng Dou, Ruihua Song, and Ji-Rong Wen. A large-scale evaluation and analysis of personalized search strategies. In Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pages 581-590, New York, NY, USA, 2007. ACM.
[21] Ahu Sieg, Bamshad Mobasher, and Robin Burke. Web search personalization with ontological user profiles. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM ’07, pages 525-534, New York, NY, USA, 2007. ACM.
[22] Jaime Teevan, Susan T. Dumais, and Eric Horvitz. Personalizing search via automated analysis of interests and activities. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’05, pages 449-456, New York, NY, USA, 2005. ACM.
[23] Milad Shokouhi. Learning to personalize query auto-completion. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’13, pages 103-112, New York, NY, USA, 2013. ACM.
[24] Jaime Teevan, Susan T. Dumais, and Eric Horvitz. Potential for personalization. ACM Trans. Comput.-Hum. Interact., 17(1):4:1-4:31, New York, NY, USA, 2010.
[25] Xuehua Shen, Bin Tan, and ChengXiang Zhai. Implicit user modeling for personalized search. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM ’05, pages 824-831, New York, NY, USA, 2005. ACM.
[26] https://www.comscore.com/Insights/Rankings/comScore-Releases-February-2016-US-Desktop-Search-Engine-Rankings
[27] https://www.w3schools.com/browsers/
Published
2017-12-15
How to Cite
BĂDĂRÎNZĂ, Ioan.
Analyzing the Usefulness of the User's Browser History for Generating Query Suggestions.
Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 62, n. 2, p. 57-68, dec. 2017.
ISSN 2065-9601.
Available at: <https://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/14>. Date accessed: 25 apr. 2024.
doi: https://doi.org/10.24193/subbi.2017.2.05.
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