TY - JOUR
T1 - Interactive Intent Modeling for Exploratory Search
AU - Ruotsalo, Tuukka
AU - Peltonen, Jaakko
AU - Eugster, Manuel JA
AU - Glowacka, Dorota
AU - Floréen, Patrik
AU - Myllymäki, Petri
AU - Jacucci, Giulio
AU - Kaski, Samuel
PY - 2018
Y1 - 2018
N2 - Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.
AB - Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.
UR - https://dl.acm.org/citation.cfm?id=3231593
U2 - 10.1145/3231593
DO - 10.1145/3231593
M3 - Article
VL - 36
SP - 1
EP - 46
JO - ACM TRANSACTIONS ON INFORMATION SYSTEMS
JF - ACM TRANSACTIONS ON INFORMATION SYSTEMS
SN - 1046-8188
IS - 4
M1 - 44
ER -