EARL Workshop 2025
EARL 2025 was the 2nd Workshop on Evaluating and Applying Recommender Systems with Large Language Models, held at RecSys 2025 in Prague, Czech Republic.
Call for Papers
The workshop focused on emerging techniques for LLM-based recommender systems, real-world applications, and critical challenges in trustworthy and responsible LLM-driven recommendation.
Topics
- Integrating LLMs to enhance recommender systems.
- LLM-generated data, fine-tuning, prompt engineering, and dynamic user modeling.
- Interactive, conversational, multimodal, cross-domain, and cross-lingual recommendation.
- Retrieval-augmented generation, few-shot and zero-shot learning, and reinforcement learning with human feedback.
- Scalability, efficiency, evaluation, human feedback, transparency, fairness, explainability, safety, privacy, and responsible AI.
Submission and review
Long papers were up to 10 pages, with extra pages permitted for references. Short papers were up to 5 pages, with extra pages permitted for references. Submissions used the ACM format and were non-archival, with double-blind review.
2025 dates
Call for Papers publication: April 25, 2025First Round paper submission deadline: July 10, 2025Second Round paper submission deadline: August 7, 2025Reviewer deadline: August 21, 2025Author notification: August 25, 2025Camera-ready version deadline: September 4, 2025
Workshop Activities
EARL 2025 introduced a Best Paper Award recognizing originality, technical rigor, and impact in LLM-based recommendation research. It also featured a dedicated poster presentation session during the coffee break.
Invited Talks
- LLMs for Next-Generation Recommender Systems: From Understanding User Behavior to Deployment — Wei-Wei Du (Sony Group Corporation) and Tommaso Carraro (Sony AI).
- Agentic Marketplace: The next era of recommendation system — Wenyue Hua (Microsoft Research, AI Frontier).
Accepted Papers
Oral Presentations
- AudioBoost: Increasing Audiobook Retrievability in Spotify Search with Synthetic Query Generation — Enrico Palumbo, Gustavo Penha, Alva Liu, Marcus Eltscheminov, Jefferson Carvalho dos Santos, Alice Wang, Hugues Bouchard, Humberto Corona and Michelle Tran Luu.
- A Metric for MLLM Alignment in Large-scale Recommendation — Yubin Zhang, Yanhua Huang, Haiming Xu, Mingliang Qi, Chang Wang, Jiarui Jin, Xiangyuan Ren, Xiaodan Wang and Ruiwen Xu.
- ScientiaRec: a Scientific Article Recommendation System with LLM-Driven Feature Extraction — Imen Ben Sassi.
- LLM-as-Judge: Rapid Evaluation of Legal Document Recommendation via Retrieval-Augmented Generation — Anu Pradhan, Alexandra Ortan, Apurv Verma and Madhavan Seshadri.
- LLM-based Relevance Assessment for Web-Scale Search Evaluation at Pinterest — Han Wang, Alex Whitworth, Pak Ming Cheung, Zhenjie Zhang and Krishna Kamath.
- Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting — Alexandre Andre, Gauthier Roy, Eva Dyer and Kai Wang.
Poster Presentations
- SemSR: Semantics aware robust Session-based Recommendations — Jyoti Narwariya, Priyanka Gupta, Muskan Gupta, Jyotsana Khatri and Lovekesh Vig.
- Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs — Firas Jarboui and Issa Memari.
- Powering Video Recommendations with Multimodal Embeddings Guided by LLMs — Andrii Dzhoha, Katya Mirylenka, Egor Malykh, Marco-Andrea Buchmann and Francesca Catino.
- Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context — Akshay Kekuda, Murali Mohana Krishna Dandu, Rimita Lahiri, Shiqin Cai, Sinduja Subramanian, Evren Korpeoglu and Kannan Achan.
- Uncovering Inference Computation Scaling for Feature Augmentation in Recommendation Systems — Weihao Liu, Zhaocheng Du, Haiyuan Zhao, Wenbo Zhang, Xiaoyan Zhao, Gang Wang, Zhenhua Dong, Xiao Zhang and Jun Xu.
- HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Data — Jiyoon Myung, Jihyeon Park and Joohyung Han.
- Serendipitous Recommendation with Multimodal LLM — Haoting Wang, Jianling Wang, Hao Li, Fangjun Yi, Mengyu Fu, Youwei Zhang, Yifan Liu, Liang Liu, Minmin Chen, Ed H. Chi, Lichan Hong and Haokai Lu.
- DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation (poster) — Yitong Li and Raoul Grasman.
- Come Together: How Social Agents can Improve Music Discovery — Laura Triglia, Pietro Gravino, Thomas Carette, Francesco Rea, Alessandra Sciutti and Pablo Barros.
- DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation — Jan Malte Lichtenberg, Antonio De Candia and Matteo Ruffini.
- Text2Playlist: Generating Personalized Playlists from Text on a Music Streaming Platform — Mathieu Delcluze, Clémence Vast and Léa Briand.
- ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations — Ben Kabongo, Vincent Guigue and Pirmin Lemberger.
Schedule
EARL 2025 took place on September 26, 2025 (GMT+2), in Prague. The half-day programme included a welcome note and award announcement, two invited talks, six oral presentations, and a coffee break.
Organizers and Program Committee
The 2025 organizing team included Irene Li, Ruihai Dong, Guillaume Salha-Galvan, Aonghus Lawlor, Dairui Liu, and Lei Li. The workshop was supported by a Program Committee with expertise in recommender systems and LLMs.
The 2025 Program Committee included:
- Tianwei She
- Yuang Jiang
- Jinwei Luo
- Arundhati Navada
- Boming Yang
- Yingjian Chen
- Fan Gao
- Lorenzo Xiao
- Li Kang
- Jiaying Xu
- Peilaing Zhang
- Jinghui Lu
- Ouyang Sixun
- Yanran Fu
- Hao Wu
- Zhongyi Lu
- Yingjie Niu
- Qin Ruan
- Changhong Jin
- Ruoyang Zhang
The 2025 edition has been preserved here as an archive. For current information, please return to the EARL 2026 homepage.
SUPPORTERS
