Personalized web exploration with task models Export

@inproceedings{citeulike:3065335, abstract = {Personalized Web search has emerged as one of the hottest topics for both the Web industry and academic researchers. However, the majority of studies on personalized search focused on a rather simple type of search, which leaves an important research topic - the personalization in exploratory searches - as an under-studied area. In this paper, we present a study of personalization in task-based information exploration using a system called TaskSieve. TaskSieve is a Web search system that utilizes a relevance feedback based profile, called a "task model", for personalization. Its innovations include flexible and user controlled integration of queries and task models, task-infused text snippet generation, and on-screen visualization of task models. Through an empirical study using human subjects conducting task-based exploration searches, we demonstrate that TaskSieve pushes significantly more relevant documents to the top of search result lists as compared to a traditional search system. TaskSieve helps users select significantly more accurate information for their tasks, allows the users to do so with higher productivity, and is viewed more favorably by subjects under several usability related characteristics.}, address = {New York, NY, USA}, author = {Ahn, Jae W. and Brusilovsky, Peter and He, Daqing and Grady, Jonathan and Li, Qi}, booktitle = {WWW '08: Proceeding of the 17th international conference on World Wide Web}, citeulike-article-id = {3065335}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1367497.1367499}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1367497.1367499}, doi = {10.1145/1367497.1367499}, isbn = {978-1-60558-085-2}, keywords = {adaptive-filtering, adaptive-search, information-retrieval, tasksieve, user-model}, location = {Beijing, China}, pages = {1--10}, posted-at = {2009-01-01 15:57:47}, priority = {2}, publisher = {ACM}, title = {Personalized web exploration with task models}, url = {http://dx.doi.org/10.1145/1367497.1367499}, year = {2008} }

TY - CONF ID - citeulike:3065335 L3 - citeulike-article-id:3065335 N2 - Personalized Web search has emerged as one of the hottest topics for both the Web industry and academic researchers. However, the majority of studies on personalized search focused on a rather simple type of search, which leaves an important research topic - the personalization in exploratory searches - as an under-studied area. In this paper, we present a study of personalization in task-based information exploration using a system called TaskSieve. TaskSieve is a Web search system that utilizes a relevance feedback based profile, called a "task model", for personalization. Its innovations include flexible and user controlled integration of queries and task models, task-infused text snippet generation, and on-screen visualization of task models. Through an empirical study using human subjects conducting task-based exploration searches, we demonstrate that TaskSieve pushes significantly more relevant documents to the top of search result lists as compared to a traditional search system. TaskSieve helps users select significantly more accurate information for their tasks, allows the users to do so with higher productivity, and is viewed more favorably by subjects under several usability related characteristics. CY - Beijing, China SN - 978-1-60558-085-2 AD - New York, NY, USA EP - 10 TI - Personalized web exploration with task models PB - ACM T2 - WWW '08: Proceeding of the 17th international conference on World Wide Web SP - 1 KW - adaptive-filtering KW - adaptive-search KW - information-retrieval KW - tasksieve KW - user-model AU - Ahn, Jae AU - Brusilovsky, Peter AU - He, Daqing AU - Grady, Jonathan AU - Li, Qi PY - 2008/// UR - http://dx.doi.org/10.1145/1367497.1367499 ER -