Learning Contextualized User Preferences for Co-Adaptive Guidance in Mixed-Initiative Topic Model Refinement

Simple item page
dc.contributor.authorSperrle, Fabianen_US
dc.contributor.authorSchäfer, Hannaen_US
dc.contributor.authorKeim, Danielen_US
dc.contributor.authorEl-Assady, Mennatallahen_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:01:37Z
dc.date.available2021-06-12T11:01:37Z
dc.date.issued2021
dc.description.abstractMixed-initiative visual analytics systems support collaborative human-machine decision-making processes. However, many multiobjective optimization tasks, such as topic model refinement, are highly subjective and context-dependent. Hence, systems need to adapt their optimization suggestions throughout the interactive refinement process to provide efficient guidance. To tackle this challenge, we present a technique for learning context-dependent user preferences and demonstrate its applicability to topic model refinement. We deploy agents with distinct associated optimization strategies that compete for the user's acceptance of their suggestions. To decide when to provide guidance, each agent maintains an intelligible, rule-based classifier over context vectorizations that captures the development of quality metrics between distinct analysis states. By observing implicit and explicit user feedback, agents learn in which contexts to provide their specific guidance operation. An agent in topic model refinement might, for example, learn to react to declining model coherence by suggesting to split a topic. Our results confirm that the rules learned by agents capture contextual user preferences. Further, we show that the learned rules are transferable between similar datasets, avoiding common cold-start problems and enabling a continuous refinement of agents across corpora.en_US
dc.description.number3
dc.description.sectionheadersMachine Learning and Explainable AI
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume40
dc.identifier.doi10.1111/cgf.14301
dc.identifier.issn1467-8659
dc.identifier.pages215-226
dc.identifier.urihttps://doi.org/10.1111/cgf.14301
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14301
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleLearning Contextualized User Preferences for Co-Adaptive Guidance in Mixed-Initiative Topic Model Refinementen_US
Files
Original bundle
Now showing 1 - 3 of 3
Thumbnail Image
Name:
v40i3pp215-226.pdf
Size:
1.75 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
1016-file1.mp4
Size:
21.84 MB
Format:
Unknown data format
Download
Thumbnail Image
Name:
cgf40-3_14301_ProjektDeal.pdf
Size:
1.87 MB
Format:
Adobe Portable Document Format
Description:
Projekt Deal version
Download
Collections
40-Issue 3