Proactive Information Retrieval by User Modeling from Eye Tracking
author:
Jarkko Salojärvi,
Helsinki University of Technology
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| Slides | |
| 0:00 | Proactive Information Retrieval by User Modeling from Eye Tracking |
| 0:14 | People can infer a lot from gaze |
| 0:45 | Proactive user interface |
| 1:25 | Gaze direction and target can be measured pt 1 |
| 2:17 | Gaze direction and target can be measured pt 2 |
| 3:19 | Pilot study: Inferring relevance from eye movements |
| 3:35 | Experimental setup |
| 4:36 | Pascal NoE Challenge |
| 5:19 | There are other sources of implicit feedback as well pt 1 |
| 6:06 | There are other sources of implicit feedback as well pt 2 |
| 6:40 | Case study: Infer the relevance of titles of scientific articles |
| 6:57 | Setting |
| 7:27 | Eye movements |
| 7:57 | Feature extraction |
| 8:38 | Predicting relevance with Discriminative Hidden Markov Models |
| 9:25 | Performance measures |
| 9:53 | Results pt 1 |
| 10:28 | Collaborative Filtering = Relevance out of others’ interests pt 1 |
| 10:32 | Collaborative Filtering = Relevance out of others’ interests pt 2 |
| 11:33 | Experimental setup pt 1 |
| 11:48 | Experimental setup pt 2 |
| 11:56 | User Rating Profile (URP) Model pt 1 |
| 12:18 | User Rating Profile (URP) Model pt 2 |
| 12:21 | Results pt 2 |
| 13:10 | Combining collaborative filtering and eye movements |
| 13:14 | Combining predictions |
| 13:43 | Dirichlet Mixture Model pt 1 |
| 14:30 | Dirichlet Mixture Model pt 2 |
| 14:32 | Results pt 3 |
| 15:20 | Eyeogle |
| 16:25 | Conclusions |
| 17:32 | The research consortium |
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