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Learn to adapt based on users' feedback
The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 2014Adaptive and personalized behavior is becoming essential and desirable in Human-Robot Interactive systems. We are interested in adaptive robots that learn from interaction traces (previous interactions with users). Our proposal is based on types of interactions where users express their level of satisfaction through feedback.
Karami, Abir Béatrice +2 more
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FedRec: Federated Recommendation With Explicit Feedback
IEEE Intelligent Systems, 2021Recommendation models have been widely embedded in various online services, while most of which are designed with the assumption that users’ original behaviors are available in a central server. This may cause the privacy issue.
Guanyu Lin +3 more
semanticscholar +1 more source
Incorporating user feedback in embodied evolution
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017We investigate the possibilities of incorporating user feedback at run-time in an embodied evolution setting. User feedback in this case consists of a user ranking a small sample from the population in an ongoing evolutionary process according to some criterion.
Kemeling, Micha, Haasdijk, Evert
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SURF: improving classifiers in production by learning from busy and noisy end users
International Conference on AI in Finance, 2020Supervised learning classifiers inevitably make mistakes in production, perhaps mis-labeling an email, or flagging an otherwise routine transaction as fraudulent.
J. Lockhart +5 more
semanticscholar +1 more source
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
ACM Multimedia, 2020Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interaction graph, edges between user and item nodes function as the main element
Yin-wei Wei +4 more
semanticscholar +1 more source
Forming user models by understanding user feedback
User Modeling and User-Adapted Interaction, 1994An intelligent advisory system should be able to provide explanatory responses that correct mistaken user beliefs. This task requires the ability to form a model of the user's relevant beliefs and to understand and address feedback from users who are not satisfied with its advice.
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Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2016
A strong User Experience (UX) discipline has become a business imperative across commercial industry. Accordingly, Human Factors professionals may be part of UX teams in large organizations designing enterprise systems (business-to-business technologies that serve as corporate back-ends or enabling technologies for other products).
Danielle Smith +4 more
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A strong User Experience (UX) discipline has become a business imperative across commercial industry. Accordingly, Human Factors professionals may be part of UX teams in large organizations designing enterprise systems (business-to-business technologies that serve as corporate back-ends or enabling technologies for other products).
Danielle Smith +4 more
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Destructive Feedback: a user created strategy for collecting user feedback in shared systems. [PDF]
, 2023 This paper documents a method for collecting user feedback on broken or malfunctioning devices dubbed Destructive Feedback; where the user deliberately “breaks” the device by removing an affordance. This makes it easier to detect visually and with sensors, as well as discourages others from using a broken device.
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Template Parsing with User Feedback
2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC'05), 2005Spreadsheets are among the most widely used end-user programming systems. According to some estimates, up to 90% of spreadsheets have non-trivial errors in them (Rajalingham et al., 2001). In many cases, spreadsheet errors have resulted in huge financial losses for companies.
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Learning User Preferences Without Feedbacks
2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021Recommending relevant data is vital for helping users to navigate through the ocean of data. We developed a service that learns user preferences through natural user interactions, without asking for user feedbacks, so users are not distracted from their regular workflow.
Wei Zhang 0249, Chris Challis
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