gaze prediction for recommender systems
TRANSCRIPT
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Gaze Prediction for Recommender Systems
Qian Zhao, Shuo Chang, F. Max Harper, Joseph A. Konstan
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Why gaze?
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Why gaze: users are thinking a lot!
Go beyond normally logged ratings and actions
Understand what users are thinking Choice/decision making research Cognitive modeling
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Why gaze?
Understand user inaction Do users see the displayed items?
Problem with machine learning assumptions Are positive training instances really
positive? Are negative training instances really
negative?
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However
Eye tracking is not widely used (and in the near future)
Eye tracking may not be widely used.
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Lets model and predict gaze
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Aggregated Fixation Prediction
Consider browsing one page in a grid-based interface (r rows * c columns)
Aggregating entire page browsing, predict each displayed items fixation probability fixation time
Given user browsing data item positions, page dwell time, user
actions (top-down vs. bottom-up)
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Two scenarios
Training models only based user browsing data
Training models with both user browsing data eye tracking data from a small number of
users Evaluation Extrapolation (across users)
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Data Sets - MovieLens
November 2015, user browsing data 102K page views
17 subjects eye tracking data, each recorded for ~30 mins 452 page views, 10K data points Tasks: free using, rating, finding movies
etc.
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Building Linear Models
Logistic regression for fixation probability
Hurdle linear models for fixation time Features
Position: row index and column index Dwell time 1/minActionDist
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Building HMM
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Fixation (latent or observable): F r * c possible values
Action (latent or observable): A r * c + 1 possible values
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Building HMM
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Estimation with eye tracking data (MLE) with only browsing data (EM or Appr.)
Prediction based on posterior of F
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Evaluation
Randomly pick 20% of the 17 subjects for testing, others for training
Repeat for 100 times but always using a different set of testing subjects (independence)
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Fixation Probability AUC
Collecting eye tracking data greatly
helps and it extrapolates across users. HMM is better than linear models
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Action stats Linear models HMM Training with only browsing
data 0.580 N.A. 0.693
Training with eye tracking and
browsing data - 0.757 0.823
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Fixation Time MAE
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Collecting eye tracking data significantly helps.
Hurdle linear model is better than HMM R-squared: 21%
Action stats Linear models HMM Training with only browsing
data 0.466 N.A. 0.520
Training with eye tracking and
browsing data - 0.332 0.488
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F-pattern (vs. center effect)
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Messages from this talk
Gaze prediction extrapolates across users! Collecting eye tracking data from a small
number of users greatly help. Applying the right models makes a
significant difference. HMMs for fixation probability. Hurdle linear models for fixation time.
F-pattern instead of center effect
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Thanks! Questions?
Title: Gaze Prediction for Recommender Systems See the paper for more results on other HMM
models and prediction for different user tasks!
Authors: Qian Zhao, Shuo Chang, F. Max Harper, Joseph A. Konstan
Contact [email protected] http://www-users.cs.umn.edu/~qian/
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mailto:[email protected]
Gaze Prediction for Recommender SystemsWhy gaze? 3Why gaze: users are thinking a lot!Why gaze?HoweverLets model and predict gazeAggregated Fixation PredictionTwo scenariosData Sets - MovieLensBuilding Linear ModelsBuilding HMMBuilding HMMEvaluationFixation Probability AUCFixation Time MAEF-pattern (vs. center effect)Messages from this talkThanks! Questions?