Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,007 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle