PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones
Bibliographic record
Abstract
As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users’ inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users’ inner feelings, we consider using the change of pupil size when browsing to model users’ preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users’ pupil size change information through the front camera of smartphones; it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user–product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users’ preference and can recommend products users interested in.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".