Información de la conferencia

RecSys 2026: ACM Conference on Recommender Systems

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Día de Entrega:
2026-04-14
Fecha de Notificación:
2026-07-09
Fecha de conferencia:
2026-09-28
Ubicación:
Minneapolis, Minnesota, USA
Ediciones:
20
CCF: B   ICORE: A   QUALIS: B1   Vistas: 56757   Seguidores: 60   Asistentes: 8

Índice Conference Partner (CP-I)

84,1 / 100
Puesto n.º 139 de 5.645 congresos · 3% superior

N.º 8 de 253 en Sistemas de información y Web

Reconocimiento académico (35%)
88
Selectividad en la revisión (20%)
92
Ediciones celebradas (20%)
82
Atención de la comunidad (10%)
65
Integridad del registro público (15%)
80

Datos utilizados: Categorías: CCF B, ICORE A, QUALIS B1 · Tasa de aceptación: 18.7% (media de 5 ediciones registradas) · Ediciones documentadas: 20 · Investigadores que lo siguen aquí: 60 · Investigadores que abrieron esta página en los últimos 24 meses: 28

Falta en el registro público: Ediciones anteriores (+3,0)
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Solicitud de Artículos

RecSys 2026 (ACM Conference on Recommender Systems) is a CCF B / ICORE A / QUALIS B1 conference held in Minneapolis, Minnesota, USA on 2026-09-28. The paper submission deadline is 2026-04-14. Acceptance notifications are sent on 2026-07-09.

The 20th ACM Conference on Recommender Systems (RecSys 2026), the leading conference for research on the foundations and applications of recommendation technologies, will take place from September 28 to October 2nd, 2026, in Minneapolis, Minnesota, USA. We look forward to receiving your contributions for RecSys 2026. Below, you will find the descriptions of different tracks accepting contributions. The “In-Brief” and “Important Dates” sections of the CFP discuss key points of attention for this call. In the rest of the CFP, we provide detailed information that authors should thoroughly review when preparing their submissions. Main track (handled by the program chairs): Long papers: We welcome high-impact original papers that contribute to all aspects of recommender systems. The paper length should be commensurate with the depth of contribution, comprehensiveness of analyses, and thorough discussion of related work. Each accepted paper will be included in the conference proceedings and presented at the conference. We expect the review process to be highly selective. Short papers: This track is intended for contributions that can be described completely and rigorously within a smaller page limit. These papers should present focused, self-contained research stories supported by experimental validations. Past, present and future papers: To mark the twentieth year of the RecSys Conference, this track encourages papers that consider a broad perspective on how the field has evolved and the challenges and directions that lay ahead. Relevant Areas & Topics Foundations of recommender systems Human-centered and interactive recommendation Explainability, transparency, and user control in recommender systems Fairness, safety, diversity, bias-mitigation, and societal impact of recommender systems Legal and ethical aspects of recommender systems Sustainable and eco-aware recommender systems Recommenders for multi-stakeholder, cross-domain, and multimodal contexts Generative, agentic, and reasoning-based recommendation Conversational, knowledge-based, and context-aware recommenders Recommendation evaluation methodologies and metrics beyond accuracy Recommender systems data, reproducibility, and benchmarking resources Real-world applications, case studies, and deployment insights of recommenders Other tracks (handled by their respective chairs): Research and Practice Notes (replacing late-breaking results): short presentations of preliminary work, mainly focused on fostering discussions with other members of the RecSys community. Demo: implementations of novel, interesting, and important recommender systems’ concepts or applications. Reproducibility: contributions that discuss several aspects of reproducibility of empirical results, such as new resources or novel evaluation methodologies. Industry: papers that discuss field experiences, deployments, user studies and real-world challenges faced by industry practitioners.
Última actualización por Dou Sun el

Tasa de aceptación

Tasa de aceptación media: 19.4% en 6 años (2015–2020).

AñoEnviadosAceptadosAceptados(%)
20202183917.9%
20191893619%
20181813217.7%
20171252620.8%
20161592918.2%
20151523523%

Los Mejores Artículos

AñoLos Mejores Artículos
2025Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender Systems
2025You Don’t Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
2024Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
2024The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems
2024Towards Empathetic Conversational Recommender Systems
2023gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling
2023Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
2023Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation
2023Interpretable User Retention Modeling in Recommendation
2023Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering
2023Of Spiky SVDs and Music Recommendation
2022Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences
2022Modelling Two-Way Selection Preference for Person-Job Fit
2022Denoising Self-Attentive Sequential Recommendation
2022RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations
2022RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data
2021An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
2021Pessimistic Reward Models for Off-Policy Learning in Recommendation
2021Connecting Students with Research Advisors Through User-Controlled Recommendation
2020Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
2020Exploiting Performance Estimates for Augmenting Recommendation Ensembles
2020ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation
2019Pace my race: recommendations for marathon running
2019Are we really making much progress? A worrying analysis of recent neural recommendation approaches
2019Quick and Accurate Attack Detection in Recommender Systems through User Attributes
2018HOP-rec: High-order Proximity for Implicit Recommendation
2018Impact of Item Consumption on Assessment of Recommendations in User Studies
2018Generation Meets Recommendation: Proposing Novel Items for Groups of Users
2018Causal Embeddings for Recommendation
2017Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations
2017Translation-based Recommendation
2016Adaptive, Personalized Diversity for Visual Discovery
2016Local Item-Item Models For Top-N Recommendation
2015Context-Aware Event Recommendation in Event-based Social Networks
2015Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates
2014Beyond Clicks: Dwell Time for Personalization
2013A Fast Parallel SGD for Matrix Factorization in Shared Memory Systems
2012CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering
2012Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering
2012Alternating Least Squares for Personalized Ranking
2012Ranking With Non-Random Missing Ratings: Influence Of Popularity And Positivity on Evaluation Metrics
2011OrdRec: An Ordinal Model for Predicting Personalized Item Rating Distributions
2011Utilizing Related Product for Post-Purchase Recommendation in E-commerce
2010A Matrix Factorization Technique with Trust Propagation for Recommendation in Social
2010Merging Multiple Criteria to Identify Suspicious Reviews
2010The Network Effects of Recommending Social Connections
2009Collaborative Prediction and Ranking with Non-Random Missing Data
2009Understanding the Effect of Adaptive Preference Elicitation Methods on User Satisfaction of a Recommender System

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Revistas Relacionadas

CCFNombre CompletoFactor de ImpactoEditorISSN
CACM Transactions on Recommender SystemsACM2770-6699
BSoftware & Systems Modeling3.2Springer1619-1366
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BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
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BComputers & Security5.4Elsevier0167-4048
BIEEE Transactions on Intelligent Transportation Systems8.4IEEE1524-9050
BJournal of Systems and Software3.8Elsevier0164-1212
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