Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This is the public RecGaze dataset from the paper: RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces Link to open-acess paper: SIGIR 2025, Arxiv Follow-up eye tracking analysis of user browsing behavior: IUI 2026 Follow-up click modeling paper on observed examination position-based click models for carousels: Arxiv The dataset is also available in an improved click dataset version (interactions including clicks, impressions, and fixations are listed for all items per screen/session) suitable for click modeling that can be openly downloaded: RecGaze Click Feedback Dataset Please cite the following: @inproceedings{10.1145/3726302.3730301,author = {de Leon-Martinez, Santiago and Kang, Jingwei and Moro, Robert and de Rijke, Maarten and Kveton, Branislav and Oosterhuis, Harrie and Bielikova, Maria},title = {RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces},year = {2025},isbn = {9798400715921},publisher = {Association for Computing Machinery},address = {New York, NY, USA},url = {https://doi.org/10.1145/3726302.3730301},doi = {10.1145/3726302.3730301},booktitle = {Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},pages = {3702–3711},numpages = {10},keywords = {browsing behavior, carousel interfaces, eye tracking},location = {Padua, Italy},series = {SIGIR '25}} @inproceedings{10.1145/3742413.3789166,author = {de Leon-Martinez, Santiago and Moro, Robert and Kveton, Branislav and Bielikova, Maria},title = {Riding the Carousel: The First Extensive Eye Tracking Analysis of Browsing Behavior in Carousel Recommenders},year = {2026},isbn = {9798400719844},publisher = {Association for Computing Machinery},address = {New York, NY, USA},url = {https://doi.org/10.1145/3742413.3789166},doi = {10.1145/3742413.3789166},booktitle = {Proceedings of the 31st International Conference on Intelligent User Interfaces},pages = {2120–2130},numpages = {11},keywords = {Carousel interfaces, Multi-list recommendations, Browsing behavior, Eye tracking},location = {},series = {IUI '26}} Dataset Description The RecGaze dataset is the first comprehensive feedback dataset on carousels that includes eye tracking results, clicks, cursor movements, and selection explanations. The dataset comprises of interactions from 3 movie selection tasks with 40 different carousel interfaces per user. In total, 87 users and 3,477 interactions are logged. Public Dataset download contains: Summary Feedback Dataframe (summary_feedback.csv) - All the feedback (fixations, clicks, cursor movements) data gathered during the movie selection screens Click Feedback Dataframe (click_feedback.csv) - Summary dataframe, primarily for click modeling and other Recommender usages, that only contains the last movie selection click per user, screen pair. Item Features Dataframe (item_features.csv) - Contains all the information for the movies used to create the carousel screens along with extra data that was not used for the study. User Features Dataframe (user_features.csv) - Contains all the information gathered from the users during the pre-survey, post-survey, and post-selection screens (selection explanations). A more detailed description of all the files and their contents (along with supplementary material) can be found in the GitHub. Non-public Version The non-public version additionally contains the following (for a more in-depth explanation and examples see paper, Table 2 ): User Features Age Gender Answer to most helpful carousel topic/explanation question Summary Feedback Dataframe x,y pixel postions for fixation, cursor, clicks Raw Gaze data Other Screen recordings of every movie selection task for all users and screens For the non-public version of the dataset, request access through this link
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it