%0 Conference Paper %1 Yue:2014:MSP:2531602.2531658 %A Yue, Zhen %A Han, Shuguang %A He, Daqing %B Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &\#38; Social Computing %C New York, NY, USA %D 2014 %I ACM %K collaborative-search social-information-access %P 820--830 %R 10.1145/2531602.2531658 %T Modeling Search Processes Using Hidden States in Collaborative Exploratory Web Search %U http://doi.acm.org/10.1145/2531602.2531658 %X Investigations of search processes that involve complex interactions, such as collaborative search processes, are important research topics. Previous approaches of directly applying individual search process models into collaborative settings have proven to be problematic. In this paper, we proposed an innovative approach to model collaborative search processes using Hidden Markov Model (HMM), which is an automatic technique for analyzing temporal sequential data. Obtained through a user study, the data used in this paper consist of two different tasks in both collaborative exploratory Web search and individual exploratory Web search conditions. Our results showed that the identified hidden patterns of search process through HMM are compatible with previous well-known models. In addition, HMM generates detailed information on the transitions of hidden patterns in search processes, which demonstrated to be useful for analyzing task differences, and for determining the correlation of search process with search performance. The findings can be used for evaluating collaborative search systems as well as providing guidance for the system design. %@ 978-1-4503-2540-0

@inproceedings{Yue:2014:MSP:2531602.2531658, abstract = {Investigations of search processes that involve complex interactions, such as collaborative search processes, are important research topics. Previous approaches of directly applying individual search process models into collaborative settings have proven to be problematic. In this paper, we proposed an innovative approach to model collaborative search processes using Hidden Markov Model (HMM), which is an automatic technique for analyzing temporal sequential data. Obtained through a user study, the data used in this paper consist of two different tasks in both collaborative exploratory Web search and individual exploratory Web search conditions. Our results showed that the identified hidden patterns of search process through HMM are compatible with previous well-known models. In addition, HMM generates detailed information on the transitions of hidden patterns in search processes, which demonstrated to be useful for analyzing task differences, and for determining the correlation of search process with search performance. The findings can be used for evaluating collaborative search systems as well as providing guidance for the system design.}, acmid = {2531658}, added-at = {2017-04-24T16:38:23.000+0200}, address = {New York, NY, USA}, author = {Yue, Zhen and Han, Shuguang and He, Daqing}, biburl = {https://www.bibsonomy.org/bibtex/2008d5ca280adc4e94f56106db3201fd8/angrrr}, booktitle = {Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work \&\#38; Social Computing}, description = {Modeling search processes using hidden states in collaborative exploratory web search}, doi = {10.1145/2531602.2531658}, interhash = {fe67501786b7edc727234e3c4c8c09a7}, intrahash = {008d5ca280adc4e94f56106db3201fd8}, isbn = {978-1-4503-2540-0}, keywords = {collaborative-search social-information-access}, location = {Baltimore, Maryland, USA}, numpages = {11}, pages = {820--830}, publisher = {ACM}, series = {CSCW '14}, timestamp = {2017-04-24T16:38:23.000+0200}, title = {Modeling Search Processes Using Hidden States in Collaborative Exploratory Web Search}, url = {http://doi.acm.org/10.1145/2531602.2531658}, year = 2014 }