%0 Conference Paper %1 Umemoto:2012:SIE:2254556.2254624 %A Umemoto, Kazutoshi %A Yamamoto, Takehiro %A Nakamura, Satoshi %A Tanaka, Katsumi %B Proceedings of the International Working Conference on Advanced Visual Interfaces %C New York, NY, USA %D 2012 %I ACM %K Intention informationSeeking support %P 349--356 %R 10.1145/2254556.2254624 %T Search Intent Estimation from User's Eye Movements for Supporting Information Seeking %U http://doi.acm.org/10.1145/2254556.2254624 %X In this paper, we propose a two-stage system using user's eye movements to accommodate the increasing demands to obtain information from the Web in an efficient way. In the first stage the system estimates a user's search intent as a set of weighted terms extracted based on the user's eye movements while browsing Web pages. Then in the second stage, the system shows relevant information to the user by using the estimated intent for re-ranking search results, suggesting intent-based queries, and emphasizing relevant parts of Web pages. The system aims to help users to efficiently obtain what they need by repeating these steps throughout the information seeking process. We proposed four types of search intent estimation methods (MLT, nMLT, DLT and nDLT) considering the relationship among intents, term frequencies and eye movements. As a result of an experiment designed for evaluating the accuracy of each method with a prototype system, we confirmed that the nMLT method works best. In addition, by analyzing the extracted intent terms for eight subjects in the experiment, we found that the system could estimate the unique search intent of each user even if they performed the same search tasks. %@ 978-1-4503-1287-5

@inproceedings{Umemoto:2012:SIE:2254556.2254624, abstract = {In this paper, we propose a two-stage system using user's eye movements to accommodate the increasing demands to obtain information from the Web in an efficient way. In the first stage the system estimates a user's search intent as a set of weighted terms extracted based on the user's eye movements while browsing Web pages. Then in the second stage, the system shows relevant information to the user by using the estimated intent for re-ranking search results, suggesting intent-based queries, and emphasizing relevant parts of Web pages. The system aims to help users to efficiently obtain what they need by repeating these steps throughout the information seeking process. We proposed four types of search intent estimation methods (MLT, nMLT, DLT and nDLT) considering the relationship among intents, term frequencies and eye movements. As a result of an experiment designed for evaluating the accuracy of each method with a prototype system, we confirmed that the nMLT method works best. In addition, by analyzing the extracted intent terms for eight subjects in the experiment, we found that the system could estimate the unique search intent of each user even if they performed the same search tasks.}, acmid = {2254624}, added-at = {2017-12-06T09:20:54.000+0100}, address = {New York, NY, USA}, author = {Umemoto, Kazutoshi and Yamamoto, Takehiro and Nakamura, Satoshi and Tanaka, Katsumi}, biburl = {https://www.bibsonomy.org/bibtex/2531d5e3cd5bcf67278a1abf528a3c5ac/dagmarkern}, booktitle = {Proceedings of the International Working Conference on Advanced Visual Interfaces}, description = {Search intent estimation from user's eye movements for supporting information seeking}, doi = {10.1145/2254556.2254624}, interhash = {64376fbfdb91483aee9bbc5082f8a015}, intrahash = {531d5e3cd5bcf67278a1abf528a3c5ac}, isbn = {978-1-4503-1287-5}, keywords = {Intention informationSeeking support}, location = {Capri Island, Italy}, numpages = {8}, pages = {349--356}, publisher = {ACM}, series = {AVI '12}, timestamp = {2017-12-06T09:20:54.000+0100}, title = {Search Intent Estimation from User's Eye Movements for Supporting Information Seeking}, url = {http://doi.acm.org/10.1145/2254556.2254624}, year = 2012 }