{"id":2228,"date":"2025-11-01T18:30:51","date_gmt":"2025-11-01T18:30:51","guid":{"rendered":"https:\/\/wsdm-conference.org\/2026\/?page_id=2228"},"modified":"2026-01-08T17:04:40","modified_gmt":"2026-01-08T17:04:40","slug":"workshops","status":"publish","type":"page","link":"https:\/\/wsdm-conference.org\/2026\/index.php\/workshops\/","title":{"rendered":"Workshops"},"content":{"rendered":"
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List of Accepted Workshops<\/h4>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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GenAI4SM: Generative AI for Streaming Media<\/h4>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Organizers: <\/strong>Amit Goyal, Vladan Radosavljevic, Sudarshan Lamkhede, Praveen Chandar, Arnab Bhadury and Tao Ye<\/p>\n

Description:<\/strong> Streaming media consumption has grown dramatically, becoming integral to daily life across all ages. Powered by machine learning (ML), streaming platforms are now among the most visible and influential\n applications of AI. However, research on generative AI (GenAI) for streaming media remains fragmented across conferences, and the gap between academic research and industry needs limits real-world impact. To address this, we aim\n to: (1) bridge academia\u2013industry collaboration, (2) attract GenAI and ML researchers to streaming media challenges, and (3) highlight industry pain points requiring academic attention. Motivated by these goals, we are organizing\n the Generative AI for Streaming Media (GenAI4SM) workshop at WSDM 2026. We invite submissions of original research, preliminary results, and new proposals. All papers will be peer reviewed for relevance and potential impact. Accepted\n works will be presented at the workshop and included in the WSDM 2026 companion proceedings.<\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Third Workshop on Generative AI for Recommender Systems and Personalization<\/h4>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Organizers: <\/strong>Narges Tabari, Aniket Deshmukh, Wang-Cheng Kang, Julian McAuley, James Caverlee, Neil Shah and George Karypis<\/p>\n

Description:<\/strong> Personalization is key in understanding user behavior and has been a main focus in the fields of knowledge discovery and information retrieval. Building personalized recommender systems is especially important\n now due to the vast amount of user-generated textual content, which offers deep insights into user preferences. The recent advancements in Large Language Models (LLMs) have significantly impacted research areas, mainly in Natural\n Language Processing and Knowledge Discovery, giving these models the ability to handle complex tasks and learn context. However, the use of generative models and user-generated text for personalized systems and recommendations is\n relatively new and has shown some promising results. This workshop is designed to bridge the research gap in these fields and explore personalized applications and recommender systems. We aim to fully leverage generative models to\n develop AI systems that are not only accurate but also focused on meeting individual user needs. Building upon the momentum of previous successful forums, this workshop seeks to engage a diverse audience from academia and industry,\n fostering a dialogue that incorporates fresh insights from key stakeholders in the field.<\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Workshop on Benchmarking Causal Models (CausalBench)<\/h4>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Organizers: <\/strong>K. Selcuk Candan, Ruocheng Guo, Huan Liu and Paras Sheth<\/p>\n

Description:<\/strong> Recent advances in causal machine learning have produced numerous models for causal discovery and inference across domains like web search and data mining. However, the field lacks unified, publicly available,\n and configurable benchmarks supporting key causal inference tasks, such as discovery, effect estimation, and inference. The WSDM Workshop on Benchmarking Causal Models (CausalBench\u201926) aims to accelerate progress in causal learning\n by fostering collaboration on datasets, algorithms, and evaluation metrics, while advancing scientific objectivity, transparency, and fairness. CausalBench\u201926 invites extended abstracts and research papers on (a) open-source platforms\n for data exchange, benchmarking, and reproducible evaluation of causal learning algorithms, (b) novel causal discovery and inference algorithms with reproducible benchmarking results, (c) causality-inspired methods, datasets, or\n metrics for benchmarking any aspect of trustworthiness of AI systems and methods, and (d) real-world applications and demonstrations of causal benchmarking.<\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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WEB&GRAPH 2026 \u2014 First Workshop on Web & Graphs, Responsible Intelligence, and Social Media<\/h4>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Organizers: <\/strong>Matteo Spezialetti, Andrea D’Angelo, Francesca Ciccarelli, Giuseppe Costanzo, Daniele Fossem\u00f2, Filippo Mignosi<\/p>\n

Description:<\/strong> Graphs are an abstraction for modeling structured and interconnected web data, encompassing hyperlinks, social networks, knowledge graphs, and user\u2013item interactions. Recent advances in graph neural networks\n (GNNs), graph transformers, and large language models (LLMs) have opened up new opportunities for integrating symbolic, relational, and textual reasoning. Yet, ensuring adaptivity, interpretability, and trustworthiness in web-scale\n graph systems remains a grand challenge. The WEB&GRAPH 2026 (First Workshop on Web & Graphs, Responsible Intelligence, and Social Media) aims to bring together researchers and practitioners from web search, data mining,\n artificial intelligence, and social sciences to discuss algorithmic, theoretical, and methodological advances for dynamic, reliable, and human-aligned graph analytics. The workshop will focus on the algorithmic foundations and the\n applied aspects of graph reasoning for evolving networks, misinformation detection, provenance tracking, and human\u2013AI collaboration. By fostering interdisciplinary dialogue, WEB&GRAPH 2026 will help the next generation of intelligent,\n explainable, and socially aware graph-based web systems.<\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Ethics and Inclusive Collaboration<\/h4>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Organizers: <\/strong>Leonie Thieme, Marcel Schack, Tereza Iofciu<\/p>\n

Description:<\/strong> The WSDM 2026 Workshop Series on Ethics and Inclusive Collaboration provides a forum for researchers and practitioners to reflect on ethical practices and inclusivity in STEM research environments. The\n series covers topics such as power dynamics, privilege, communication norms, and inclusive academic culture, aiming to raise awareness and foster more equitable and respectful collaboration within the research community.<\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

List of Accepted Workshops GenAI4SM: Generative AI for Streaming Media Organizers: Amit Goyal, Vladan Radosavljevic, Sudarshan Lamkhede, Praveen Chandar, Arnab Bhadury and Tao Ye Description: Streaming media consumption has grown dramatically, becoming integral to daily life across all ages. Powered by machine learning (ML), streaming platforms are now among the most visible and influential applications of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width-page.php","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/wsdm-conference.org\/2026\/index.php\/wp-json\/wp\/v2\/pages\/2228"}],"collection":[{"href":"https:\/\/wsdm-conference.org\/2026\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wsdm-conference.org\/2026\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wsdm-conference.org\/2026\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wsdm-conference.org\/2026\/index.php\/wp-json\/wp\/v2\/comments?post=2228"}],"version-history":[{"count":4,"href":"https:\/\/wsdm-conference.org\/2026\/index.php\/wp-json\/wp\/v2\/pages\/2228\/revisions"}],"predecessor-version":[{"id":2797,"href":"https:\/\/wsdm-conference.org\/2026\/index.php\/wp-json\/wp\/v2\/pages\/2228\/revisions\/2797"}],"wp:attachment":[{"href":"https:\/\/wsdm-conference.org\/2026\/index.php\/wp-json\/wp\/v2\/media?parent=2228"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}