Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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
The KDD conference has seen remarkable growth since its origins as an IJCAI workshop in Detroit in 1989, evolving into a full-fledged research conference in 1995, underscoring the important role data mining as a field has played in extracting knowledge and actionable insights from vast troves of data that is being generated in the digital world around us. This year we received a record 755 submissions to the research program, from which 133 papers were accepted, for an aggregate acceptance rate of 17.6% (quite similar to recent years). Among the academic conferences, the KDD conference has typically more of an emphasis on research motivated by real-world applications. It is important to keep in mind that it is this synergy of research in areas like algorithms, computational geometry, database, graph theory, machine learning, natural language processing, statistics, visualization and many others when applied to problems arising in diverse fields such as web, medicine, climatology, marketing that drives our field forward, makes it vibrant and fun - who would know that ideas in computational geometry can be adapted to construct fast algorithms to improve online advertising and movie recommendations? The breadth of topics covered in this year's research program is truly comprehensive, including social networks, privacy, text mining, predictive modeling, time-series forecasting, spatial data analysis, geometry, and more. We are very fortunate to have 4 world-class keynote speakers this year spanning industry and academia, providing inspirational talks on cutting-edge techniques and issues in web mining, information networks, statistical inference for big data, and social computing. The process of whittling down the initial 734 submissions to the final set of 133 accepted papers required the coordination and time of a large number of willing volunteers. The program committee (PC) consisted of over 350 reviewers (PC members) and 50 senior PC members. In the first phase each submitted paper was automatically assigned to 3 reviewers (after a bidding process). Once the reviews from each of the 3 reviewers were completed, the program chairs rejected papers that did not receive much support from any of the reviewers. We rejected 259 papers at this stage. Special care was taken to minimize the error of rejecting a potentially good paper at this stage. The papers that survived the first phase were assigned to the senior PC members based on their bids, they had the option of initiating a discussion for any of their papers, e.g., if there was significant divergence in scores among reviewers, or if a paper was on the borderline of being accepted. Following the discussion phase, the senior PC members provided a recommendation score and a detailed meta-review for each paper. In the final phase, we (the program chairs) analyzed all of this information, starting with the obvious accept and reject decisions, and then gradually focusing in more detail on the papers near the borderline, seeking additional reviews and input from the PC and senior PC members where appropriate. We also initiated a shepherding phase with 15 papers having the opportunity of fixing mild issues we thought would be possible to address before they can be accepted. 13 of them were accepted after thorough revisions. Finally, it is quite likely that in hindsight some worthy papers may have been rejected as part of this process - these errors are an unfortunate reality of modern computer science conferences, and hard to avoid when a very large number of decisions have to be made over a short time span based on a subjective reviewing process. Nevertheless, we, the PC chairs, are responsible for those unfortunate errors and welcome suggestions on the matter.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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