Research on Eliciting Users’ Implicit Feedback for Constructing their Multi-attribute Preferences in Complex Decision Support (Li Chen et al.)

The novelty of this project is that we will exert to exploit users’ implicit behavior, by means of combining their clicking actions and eye movements, for eliciting their preferences. To be more specific, we attempt to develop a novel critiquing-based recommender system based on implicitly elicited feedback, so as to reduce users’ involvement efforts while still allowing them to obtain high decision accuracy. This work is motivated by our recent preliminary research which shows the promising potential of basing implicit behavior, especially eye-gaze variables, to explain users’ attentive interests. We expect that our work will contribute to the field of complex decision support from the following aspects. 1) The analysis of users’ implicit behavior may enable us to in depth understand in which condition, and how, users perform compensatory decision strategy. 2) The implicit preference elicitation could be conducted at both product and attribute levels for potentially more accurately building users’ multi-attribute preference model. Particularly, the attribute-level elicitation may help verify the preferential dependency relationship between attributes. 3) The uncertainty of elicited user preferences could further be addressed by suggesting the attributes tradeoff relations for users to consider and choose. 4) The proposed methods will be evaluated in proof-of-concept system (with both desktop and mobile versions), through experimental simulations and controlled user studies.

Grant Support:

This project is supported by the Research Grants Council (RGC), Hong Kong SAR, China (Research on eliciting users’ implicit feedback for constructing their multi-attribute preferences in complex decision support HKBU12200415).

For further information on this research topic, please contact Dr. Li Chen.