Workshops
- Online Adaptive Recommender System (OARS) OARS 26
About
Online and Adaptive Recommender Systems (OARS) will bring together practitioners and researchers from academia and industry to discuss the challenges and new approaches to implement OARS algorithms and systems and improve user experiences by better modeling and responding to user intent.
Submissions due: July 20, 2026 — https://oars-workshop.github.io/index.html
- Workshop on Context-Aware Recommender Systems CARS 26
About
The Context-Aware Recommender Systems (CARS) workshop explores the evolving role of contextual information in modern recommender systems. Recent advances highlight the importance of modeling dynamic, implicit, and complex contextual signals beyond traditional static factors. CARS 2026 aims to bring together researchers and practitioners to discuss novel approaches, including sequence-aware and latent context modeling, real-time context inference, and context representation learning. The workshop also emphasizes emerging application domains, such as education, financial, and health systems, where capturing rich contextual signals is essential for delivering relevant, personalized, and trustworthy recommendations.
Submissions due: July 20, 2026 — https://cars-workshops.com/
- Second International Workshop on Data Quality-Aware Multimodal Recommendation DaQuaMRec 26
About
The “Data Quality-Aware Multimodal Recommendation” (DaQuaMRec) workshop focuses on a fundamental challenge in modern recommender systems: the quality of the multimodal data they rely on. As recommendation models increasingly combine text, images, audio, and other signals, they enable richer personalization and more accurate suggestions across domains such as fashion, music, food, and digital media. Advances in deep learning and foundation models have accelerated progress in this space, but they have also made clear that model performance and fairness depend heavily on the reliability of the underlying data.
DaQuaMRec provides a venue for researchers and practitioners to examine challenges such as noisy inputs, missing modalities, misaligned data, and embedded biases. These issues can undermine recommendation quality and lead to inaccurate or unfair outcomes. Through this workshop, we aim to encourage new research and practical solutions for understanding, evaluating, and improving data quality in multimodal recommendation.
Submissions due: July 20, 2026 — https://sites.google.com/view/daquamrec2026
- 13th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems IntRS 26
About
Research on Human-AI collaboration focuses on building recommender systems that work effectively with people by supporting human-in-the-loop interaction, explainability, and user-centered design. The goal of IntRS workshop is to focus on transparent, adaptable, and inclusive systems that respect autonomy, privacy, and individual differences while improving decision-making, well-being, and social good.
Submissions due: TBA — https://sites.google.com/view/intrs26
- Temporal Reasoning in Recommendation Systems RecTemp 26
About
RecTemp 2026 focuses on advancing temporal reasoning in recommender systems, addressing how evolving user preferences, sequential interactions, and contextual dynamics can be modeled over time. This year, the workshop highlights the growing role of large language models (LLMs) in time-aware recommendation, including their ability to reason over user behavior across sessions and longer horizons. We invite contributions on novel methods, empirical studies, and emerging ideas that integrate temporal dynamics into modern recommender systems.
Submissions due: July 20, 2026 — https://rectemp.com/
- Sixth Workshop on Recommender Systems for Human Resources RecSys in HR 26
About
The field of Human Resources (HR) is at the forefront of adopting AI technologies. According to PWC, over 40% of HR-functions of international companies use AI-applications. This so-called HR Technology (HR Tech) aims to replace or support Human Resource functions such as talent acquisition and management, employee compensation, workforce analytics, and performance management. Recommender Systems play an integral role in the rapid rise of HR Tech, ranging from assisting the talent acquisition process through matching and candidate screening, to broader tasks such as recommendations for upskilling. Alongside their growing use within the HR space, the European Commission has labeled such systems as “high-risk”, as automation here can directly impact the working lives of people.
Submissions due: July 20, 2026 — https://recsyshr.aau.dk/
- Methodology First: Rethinking Research Assessment in RecSys FRAME 26
About
Recommender systems is a strongly empirical field, and existing literature has highlighted several methodological problems that make published findings harder to interpret, compare, and generalize, while contributing to a gap between academic evaluation and industry needs. FRAME aims to foster discussion and progress on stronger research assessment practices. The workshop features two tracks: a Research Papers track reviewed through a methodology-first, results-blind process, and an Experimental Methodologies track for full papers and position statements on best practices, protocol design, fair comparison, and missing resources for evaluation.
Submissions due: July 20, 2026 — https://remaplab.github.io/frame2026/
- The Fourth Music Recommender Systems Workshop MuRS 26
About
The fourth Music Recommender Systems Workshop (MuRS) provides a dedicated forum to address emerging challenges and foster cross-disciplinary collaboration in the evolving field of music recommendation.
Submissions due: Initial abstract: July 14, 2026; final submission: July 17, 2026 — https://sites.google.com/view/murs-2026
- The Third Workshop on Agentic and Generative AI for E-Commerce GenAI-eCom 26
About
The “Agentic and Generative AI for E-Commerce” workshop explores the rapidly evolving intersection of recommender systems, generative AI, and agentic AI in online retail. As AI systems evolve from passive generators to autonomous agents capable of planning, reasoning, and taking actions, e-commerce stands at the forefront of this transformation. The workshop fosters discussions on how agentic AI systems — autonomous agents that can browse, compare, negotiate, and purchase on behalf of users — alongside generative models, can transform personalization, product recommendations, content creation, and user engagement in e-commerce platforms.
E-commerce companies face challenges such as lack of quality content, subpar user experience, and sparse datasets. Generative and agentic AI offer significant potential to address these, yet scaling these technologies presents challenges including hallucination, excessive costs, latency, and ensuring safe autonomous agent behavior.
Submissions due: July 20, 2026 — https://genai-ecommerce.github.io/GenAIECommerce2026
- The 10th Workshop on Recommenders in Tourism RecTour 26
About
RecTour 2026 will be the 10th RecTour workshop. We seek to delve deeper into the evolving landscape of tourism recommendation systems by convening researchers and practitioners from diverse disciplines. By fostering interdisciplinary dialogue among experts in tourism, recommender systems, user modeling, artificial intelligence, and beyond, we aim to address the unique challenges and opportunities inherent in designing next-generation tourism recommendation systems.
Submissions due: July 20, 2026 — https://workshops.ds-ifs.tuwien.ac.at/rectour26/
- Third International Workshop on Recommender Systems for Sustainability and Social Good RecSoGood 26
About
In the rapidly changing domains of technology and sustainability, Recommender Systems (RS) play an essential role in facilitating positive social and environmental change. Assisted by AI technology and data analytics, these systems are successful in various domains, including e-commerce, sustainable energy management, social inclusion, and individual well-being. The workshop is an exclusive platform for researchers, practitioners, and platform owners to investigate the integration of sustainability into recommendation systems, encouraging collaborative action for a sustainable future.
Submissions due: July 20, 2026 — https://recsogood.github.io/recsogood2026/
- Unified Search and Recommendation Workshop USRW 26
About
Search and recommendation have long been developed in parallel, despite serving the same discovery goal over shared item universes. The rise of Large Language Models and agentic systems is collapsing this divide, with unified architectures and generative retrieval increasingly powering both query-driven and feed-driven experiences. USRW brings together researchers and practitioners from the IR and RecSys communities to define shared tasks and datasets, compare unified architectures, align evaluation across paradigms, and foster cross-pollination between industry and academia. This is the inaugural edition of USRW, a full-day workshop designed to establish unified search-and-recommendation as a mainstream research and production reality.
Submissions due: July 20, 2026 — https://usrw-workshop.github.io/2026
- The 5th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems CONSEQUENCES 26
About
An increasing amount of research understands recommender systems as a decision-making problem, instead of a pure prediction problem. This view makes it clear that recommendation decisions on real-world platforms have consequences. Actions and decisions come in many forms, from selecting rankings to LLM tokens, and their consequences also vary: algorithms are often responsible for collecting training data for the next iteration, while algorithmic decisions also impact the utility that all stakeholders can get from participating on the platform.
This is the fifth instalment of the CONSEQUENCES workshop series, which has seen significant interest from across academia and industry. Advances in the intersection of these fields foster progress in effective, efficient and fair use of logged data for both learning and evaluation, and can have a strong impact on practical systems.
Submissions due: TBA — https://sites.google.com/view/consequences2026
- Beyond Algorithms: A Workshop on the Interdisciplinarity of Recommender Systems BEYOND 26
About
BEYOND brings together interdisciplinary perspectives on recommendation and personalization beyond algorithmic research. It highlights theoretical, methodological, behavioral, organizational, ethical, and societal approaches, and explicitly welcomes contributions from outside core computing as well as work bridging computational and non-computational approaches.
Submissions due: TBA — https://beyondrecsys.github.io/2026/
- The Second AltRecSys Workshop on Reimagining Recommender Systems Research & Practice AltRecSys 26
About
AltRecSys is back! As ACM RecSys turns 20, we take this milestone as an invitation to pause, take inventory, and reflect on what our rapidly expanding field may have overlooked. The workshop creates a space for alternative, provocative, and unconventional work that challenges dominant assumptions and familiar framings in recommender systems research and practice. This second edition emphasizes highly interactive discussions among researchers, practitioners, and other stakeholders, inviting reflection not only on how far the field has come, but also on where it might go if we allow ourselves to think differently.
Submissions due: TBA — https://altrecsys.github.io/recsys26/
September 28th Workshops
All workshops on this day are half-day
Morning Workshops
Afternoon Workshops
October 2nd Workshops
Full-day Workshops
Half-day: morning
Half-day: afternoon
Workshops Chairs
- Moshe Unger, Tel Aviv University, Israel
- Li Chen, Hong Kong Baptist University, China
- Bart Knijnenburg, Clemson University, USA