Information retrieval interaction
Information retrieval interaction
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
(invited paper) A new theoretical framework for information retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
From highly relevant to not relevant: examining different regions of relevance
Information Processing and Management: an International Journal
Finding without seeking: the information encounter in the context of reading for pleasure
Information Processing and Management: an International Journal - Special issue on Information Seeking In Context (ISIC)
Helping people find what they don't know
Communications of the ACM
ACM SIGIR Forum
Theories of Information Behavior (Asist Monograph)
Theories of Information Behavior (Asist Monograph)
Information re-retrieval: repeat queries in Yahoo's logs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Affective feedback: an investigation into the role of emotions in the information seeking process
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Proceedings of the ACM International Conference on Image and Video Retrieval
The role of subjective factors in the information search process
Journal of the American Society for Information Science and Technology
An implicit feedback approach for interactive information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Towards predicting web searcher gaze position from mouse movements
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Actively predicting diverse search intent from user browsing behaviors
Proceedings of the 19th international conference on World wide web
Understanding casual-leisure information needs: a diary study in the context of television viewing
Proceedings of the third symposium on Information interaction in context
The emotional impact of search tasks
Proceedings of the third symposium on Information interaction in context
Sparse hidden-dynamics conditional random fields for user intent understanding
Proceedings of the 20th international conference on World wide web
Understanding re-finding behavior in naturalistic email interaction logs
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Recent and robust query auto-completion
Proceedings of the 23rd international conference on World wide web
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The complex and dynamic nature of search processes surrounding information seeking have been exhaustively studied. Recent studies have highlighted search processes with different intentions, such as those for entertainment purposes or re-finding a visited information object, are fundamentally different in nature to typical information seeking intentions. Despite the popularity of such search processes on the Web, they have not yet been thoroughly explored. Using a video retrieval system as a use case, we study the characteristics of four different search task types: seeking information, re-finding a particular information object, and two different entertainment intentions (i.e. entertainment by adjusting arousal level, and entertainment by adjusting mood). In particular, we looked at the cognition, emotion and action aspects of these search tasks at different phases of a search process. This follows the common assumption in the information seeking and retrieval community that a complex search process can be broken down into a relatively small number of activity phases. Our experimental results show significant differences in the characteristics of studied search tasks. Furthermore, we investigate whether we can predict these search tasks given user's interaction with the system. Results show that we can learn a model that predicts the search task types with reasonable accuracy. Overall, these findings may help to steer search engines to better satisfy searchers' needs beyond typically assumed information seeking processes.