Table of Contents

• A Language Model-Based Playlist Generation Recommender SystemEnzo Charolois-Pasqua, Eléa Vellard, Youssra Rebboud, Pasquale Lisena, Raphaël Troncy. 1-11 [doi]
• A Multi-Factor Collaborative Prediction for Review-based RecommendationJunrui Liu, Tong Li 0001, Mingliang Yu, Shiqiu Yang, Zifang Tang, Zhen Yang 0004. 12-20 [doi]
• A Non-Parametric Choice Model That Learns How Users Choose Between Recommended OptionsThorsten Krause, Harrie Oosterhuis. 21-30 [doi]
• Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference ElicitationBereket Abera Yilma, Luis A. Leiva. 31-40 [doi]
• An Off-Policy Learning Approach for Steering Sentence Generation towards PersonalizationHaruka Kiyohara, Daniel Yiming Cao, Yuta Saito, Thorsten Joachims. 41-50 [doi]
• Auditing Recommender Systems for User Empowerment in Very Large Online Platforms under the Digital Services ActMatteo Fabbri, Ludovico Boratto. 51-61 [doi]
• Beyond Immediate Click: Engagement-Aware and MoE-Enhanced Transformers for Sequential Movie RecommendationHaotian Jiang, Sibendu Paul, Haiyang Zhang, Caren Chen. 62-71 [doi]
• Breaking Knowledge Boundaries: Cognitive Distillation-enhanced Cross-Behavior Course Recommendation ModelRuoyu Li, Yangtao Zhou, Chenzhang Li, Hua Chu, Jianan Li 0003, Yuhan Bian. 72-81 [doi]
• Enhancing Online Video Recommendation via a Coarse-to-fine Dynamic Uplift Modeling FrameworkChang Meng, Chenhao Zhai, Xueliang Wang, Shuchang Liu 0006, Xiaoqiang Feng, Lantao Hu, Xiu Li 0001, Han Li 0005, Kun Gai. 82-92 [doi]
• Enhancing Sequential Recommender with Large Language Models for Joint Video and Comment RecommendationBowen Zheng 0005, Zihan Lin, Enze Liu 0005, Chen Yang, Enyang Bai, Cheng Ling, Han Li 0005, Wayne Xin Zhao, Ji-Rong Wen. 93-103 [doi]
• Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised DisentanglementYuhan Wang, Qing Xie 0002, Zhifeng Bao, Mengzi Tang, Lin Li 0001, Yongjian Liu. 104-113 [doi]
• Exploring Scaling Laws of CTR Model for Online Performance ImprovementWeijiang Lai, Beihong Jin, Jiongyan Zhang, Yiyuan Zheng, Jian Dong 0012, Jia Cheng, Jun Lei, Xingxing Wang. 114-123 [doi]
• GenSAR: Unifying Balanced Search and Recommendation with Generative RetrievalTeng Shi, Jun Xu 0001, Xiao Zhang 0034, Xiaoxue Zang, Kai Zheng 0001, Yang Song 0008, Enyun Yu. 124-134 [doi]
• GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought TokenizationLuyi Ma, Wanjia Zhang, Kai Zhao, Abhishek Kulkarni, Lalitesh Morishetti, Anjana Ganesh, Ashish Ranjan, Aashika Padmanabhan, Jianpeng Xu, Jason H. D. Cho, Praveenkumar Kanumala, Kaushiki Nag, Sumit Dutta, Kamiya Motwani, Malay Patel, Evren Körpeoglu, Sushant Kumar, Kannan Achan. 135-144 [doi]
• Heterogeneous User Modeling for LLM-based RecommendationHonghui Bao, Wenjie Wang 0007, Xinyu Lin 0001, Fengbin Zhu, Teng Sun, Fuli Feng, Tat-Seng Chua. 145-154 [doi]
• Hierarchical Graph Information Bottleneck for Multi-Behavior RecommendationHengyu Zhang 0001, Chunxu Shen, Xiangguo Sun, Jie Tan 0001, Yanchao Tan, Yu Rong 0001, Hong Cheng 0001, Lingling Yi. 155-164 [doi]
• How Do Users Perceive Recommender Systems' Objectives?Patrik Dokoupil, Ludovico Boratto, Ladislav Peska. 165-176 [doi]
• Integrating Individual and Group Fairness for Recommender Systems through Social ChoiceAmanda Aird, Elena Stefancova, Anas Buhayh, Cassidy All, Martin Homola, Nicholas Mattei, Robin Burke. 177-186 [doi]
• IP2: Entity-Guided Interest Probing for Personalized News RecommendationYoulin Wu, Yuanyuan Sun 0002, Xiaokun Zhang 0001, Haoxi Zhan, Bo Xu 0009, Liang Yang 0003, Hongfei Lin. 187-196 [doi]
• LANCE: Exploration and Reflection for LLM-based Textual Attacks on News Recommender SystemsYuyue Zhao, Jin Huang 0001, Shuchang Liu, Jiancan Wu, Xiang Wang 0010, Maarten de Rijke. 197-206 [doi]
• Lasso: Large Language Model-based User Simulator for Cross-Domain RecommendationYue Chen, Susen Yang, Tong Zhang, Chao Wang, Mingyue Cheng, Chenyi Lei, Han Li. 207-216 [doi]
• LEAF: Lightweight, Efficient, Adaptive and Flexible Embedding for Large-Scale Recommendation ModelsChaoyi Jiang, Abdulla Alshabanah, Murali Annavaram. 217-225 [doi]
• Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping UsersWeixin Chen, Yuhan Zhao 0001, Li Chen 0009, Weike Pan. 226-236 [doi]
• LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential RecommendationYunzhe Li, Junting Wang 0001, Hari Sundaram, Zhining Liu 0002. 237-246 [doi]
• LONGER: Scaling Up Long Sequence Modeling in Industrial RecommendersZheng Chai, Qin Ren, Xijun Xiao, Huizhi Yang, Bo Han, Sijun Zhang, Di Chen, Hui Lu, Wenlin Zhao, Lele Yu, Xionghang Xie, Shiru Ren, Xiang Sun, Yaocheng Tan, Peng Xu, Yuchao Zheng, Di Wu. 247-256 [doi]
• Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic HiringMesut Kaya, Toine Bogers. 257-267 [doi]
• MDSBR: Multimodal Denoising for Session-based RecommendationYutong Li, Xinyi Zhang. 268-278 [doi]
• Measuring Interaction-Level Unlearning Difficulty for Collaborative FilteringHaocheng Dou, Tao Lian, Xin Xin. 279-288 [doi]
• Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR PredictionWeijiang Lai, Beihong Jin, Yapeng Zhang, Yiyuan Zheng, Rui Zhao, Jian Dong 0012, Jun Lei, Xingxing Wang. 289-298 [doi]
• MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential RecommendationWeicong Qin, Yi Xu 0003, Weijie Yu 0003, Chenglei Shen, Xiao Zhang 0034, Ming He, Jianping Fan 0001, Jun Xu 0001. 299-308 [doi]
• Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture NetworkXu Zhao, Ruibo Ma, Jiaqi Chen, Weiqi Zhao, Ping Yang, Yao Hu 0002. 309-318 [doi]
• NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for RecommendationJinfeng Xu 0003, Zheyu Chen 0003, Shuo Yang 0011, Jinze Li 0001, Hewei Wang 0001, Wei Wang 0077, Xiping Hu, Edith C. H. Ngai. 319-329 [doi]
• Non-parametric Graph Convolution for Re-ranking in Recommendation SystemsZhongyu Ouyang, Mingxuan Ju, Soroush Vosoughi, Yanfang Ye 0001. 330-339 [doi]
• Off-Policy Evaluation and Learning for Matching MarketsYudai Hayashi, Shuhei Goda, Yuta Saito. 340-349 [doi]
• Off-Policy Evaluation of Candidate Generators in Two-Stage Recommender SystemsPeiyao Wang, Zhan Shi, Amina Shabbeer, Ben London 0001. 350-359 [doi]
• On the Reliability of Sampling Strategies in Offline Recommender EvaluationBruno L. Pereira, Alan Said, Rodrygo L. T. Santos. 360-369 [doi]
• Paragon: Parameter Generation for Controllable Multi-Task RecommendationChenglei Shen, Jiahao Zhao, Xiao Zhang 0034, Weijie Yu 0003, Ming He, Jianping Fan 0001. 370-380 [doi]
• PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery PlatformXiangyi Chen, Kousik Rajesh, Matthew Lawhon, Zelun Wang, Hanyu Li, Haomiao Li, Saurabh Vishwas Joshi, Pong Eksombatchai, Jaewon Yang, Yi-Ping Hsu, Jiajing Xu, Charles Rosenberg 0001. 381-390 [doi]
• Privacy-Preserving Social Recommendation: Privacy Leakage and CountermeasureYuyue Chen, Peng Yang 0016, Zoe Lin Jiang, Wenhao Wu, Junbin Fang, Xuan Wang 0002, Chuanyi Liu. 391-400 [doi]
• Prompt-to-Slate: Diffusion Models for Prompt-Conditioned Slate GenerationFederico Tomasi, Francesco Fabbri, Justin Carter, Elias Kalomiris, Mounia Lalmas, Zhenwen Dai. 401-410 [doi]
• 4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation SystemsHao Gu, Rui Zhong, Yu Xia, Wei Yang 0041, Chi Lu, Peng Jiang 0002, Kun Gai. 411-421 [doi]
• Recommendation and TemptationMd Sanzeed Anwar, Paramveer S. Dhillon, Grant Schoenebeck. 422-431 [doi]
• RecPS: Privacy Risk Scoring for Recommender SystemsJiajie He 0003, Yuechun Gu, Keke Chen. 432-440 [doi]
• Scalable Data Debugging for Neighborhood-based Recommendation with Data Shapley ValuesBarrie Kersbergen, Olivier Sprangers, Bojan Karlas, Maarten de Rijke, Sebastian Schelter. 441-450 [doi]
• Tag-augmented Dual-target Cross-domain RecommendationMingfan Pan, Qingyang Mao, Xu An, Jianhui Ma, Gang Zhou, Mingyue Cheng, Enhong Chen. 451-460 [doi]
• Test-Time Alignment with State Space Model for Tracking User Interest Shifts in Sequential RecommendationChangshuo Zhang, Xiao Zhang 0034, Teng Shi, Jun Xu 0001, Ji-Rong Wen. 461-471 [doi]
• USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language ModelJianyu Wen, Jingyun Wang, Cilin Yan, Jiayin Cai, Xiaolong Jiang, Ying Zhang. 472-481 [doi]
• VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP EmbeddingsRamin Giahi, Kehui Yao, Sriram Kollipara, Kai Zhao, Vahid Mirjalili, Jianpeng Xu, Topojoy Biswas, Evren Körpeoglu, Kannan Achan. 482-491 [doi]
• You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk ControlGiovanni De Toni, Erasmo Purificato, Emilia Gómez, Andrea Passerini, Bruno Lepri, Cristian Consonni. 492-502 [doi]
• A Multistakeholder Approach to Value-Driven Co-Design of Recommender Systems Evaluation Metrics in Digital ArchivesFlorian Atzenhofer-Baumgartner, Georg Vogeler, Dominik Kowald. 503-508 [doi]
• "Beyond the past": Leveraging Audio and Human Memory for Sequential Music RecommendationViet-Anh Tran, Bruno Sguerra, Gabriel Meseguer-Brocal, Léa Briand, Manuel Moussallam. 509-514 [doi]
• Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender SystemsAmir Reza Mohammadi, Andreas Peintner, Michael Müller, Eva Zangerle. 515-520 [doi]
• Beyond Visit Trajectories: Enhancing POI Recommendation via LLM-Augmented Text and Image RepresentationsZehui Wang, Wolfram Höpken, Dietmar Jannach. 521-526 [doi]
• Biases in LLM-Generated Musical Taste Profiles for RecommendationBruno Sguerra, Elena V. Epure, Harin Lee, Manuel Moussallam. 527-532 [doi]
• Collaborative Interest Modeling in Recommender SystemsYu-Ting Cheng, Yu-Yen Ho, Jyun-Yu Jiang. 533-538 [doi]
• Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group RecommendationsCedric Waterschoot, Nava Tintarev, Francesco Barile. 539-544 [doi]
• Correcting the LogQ Correction: Revisiting Sampled Softmax for Large-Scale RetrievalKirill Khrylchenko, Vladimir Baikalov, Sergei S. Makeev, Artem Matveev, Sergei Liamaev. 545-550 [doi]
• Counterfactual Inference under Thompson SamplingOlivier Jeunen. 551-557 [doi]
• D-RDW: Diversity-Driven Random Walks for News Recommender SystemsRunze Li, Lucien Heitz, Oana Inel, Abraham Bernstein. 558-563 [doi]
• Determinants of Users' Chance-Seeking Behavior in Search-Based RecommendationYuki Ninomiya, Yutaro Sone, Kazuhisa Miwa, Yuichiro Sumi, Ryosuke Nakanishi, Eiji Mitsuda, Koji Sato, Tadashi Odashima. 564-569 [doi]
• Disentangling User and Item Sequence Patterns in Sequential Recommendation Data SetsKaiyue Liu, Yang Liu 0254, Alan Medlar, Dorota Glowacka. 570-574 [doi]
• Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and SearchMatteo Attimonelli, Alessandro De Bellis, Claudio Pomo, Dietmar Jannach, Eugenio Di Sciascio, Tommaso Di Noia. 575-580 [doi]
• Emotion Vector-Based Fine-Tuning of Large Language Models for Age-Aware Teenage Book RecommendationsKate Hill, Yiu-Kai Ng, Joey Sherrill. 581-586 [doi]
• Estimating Quantum Execution Requirements for Feature Selection in Recommender Systems Using Extreme Value TheoryJiayang Niu, Qihan Zou, Jie Li 0095, Ke Deng, Mark Sanderson, Yongli Ren. 587-592 [doi]
• Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI RecommendationsAndrea Forster, Simone Kopeinik, Denis Helic, Stefan Thalmann, Dominik Kowald. 593-598 [doi]
• Failure Prediction in Conversational Recommendation SystemsMaria Vlachou. 599-604 [doi]
• Feedback-Driven Gradual Discovery for Expanding Musical PreferencesAlec Nonnemaker, Ralvi Isufaj, Zoltán Szlávik, Cynthia Liem. 605-609 [doi]
• HiDePCC: A Novel Dual-Pronged Untargeted Attack on Federated Recommendation via Gradient Perturbation and Cluster CraftingYamini Jha, Krishna Tewari, Sukomal Pal. 610-614 [doi]
• Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music RecommendationAlessandro B. Melchiorre, Elena V. Epure, Shahed Masoudian, Gustavo Escobedo, Anna Hausberger, Manuel Moussallam, Markus Schedl. 615-620 [doi]
• Large Scale E-Commerce Model for Learning and Analyzing Long-Term User PreferencesYonatan Hadar, Yotam Eshel, Tal Franji, Bracha Shapira, Michelle Hwang, Guy Feigenblat. 621-625 [doi]
• Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based InitializationAnton Pembek, Artem Fatkulin, Anton Klenitskiy, Alexey Vasilev. 626-631 [doi]
• Mitigating Latent User Biases in Pre-trained VAE Recommendation Models via On-demand Input Space TransformationDavid Penz, Gustavo Junior Escobedo Ticona, Markus Schedl. 632-636 [doi]
• Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential RecommendationWei-Wei Du, Takuma Udagawa, Kei Tateno. 637-642 [doi]
• Not One News Recommender To Fit Them All: How Different Recommender Strategies Serve Various User SegmentsHanne Vandenbroucke, Ulysse Maes, Lien Michiels, Annelien Smets. 643-648 [doi]
• On Inherited Popularity Bias in Cold-Start Item RecommendationGregor Meehan, Johan Pauwels. 649-654 [doi]
• Personalized Persuasion-Aware Explanations in Recommender SystemsHavva Alizadeh Noughabi, Behshid Behkamal, Fattane Zarrinkalam, Mohsen Kahani. 655-659 [doi]
• Popularity‑Bias Vulnerability: Semi‑Supervised Label Inference Attack on Federated Recommender SystemsKenji Shinoda, Takeyuki Sasai, Shintaro Fukushima. 660-665 [doi]
• Rethinking Overconfidence in VAEs: Can Label Smoothing Help?Woo-Seong Yun, Yeojun Choi, Yoon-Sik Cho. 666-670 [doi]
• SGCL: Unifying Self-Supervised and Supervised Learning for Graph RecommendationWeizhi Zhang 0001, Liangwei Yang, Zihe Song 0001, Henry Peng Zou, Ke Xu, Yuanjie Zhu, Philip S. Yu. 671-676 [doi]
• Stairway to Fairness: Connecting Group and Individual FairnessTheresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Falk Scholer, Christina Lioma. 677-683 [doi]
• Towards Personality-Aware Explanations for Music Recommendations Using Generative AIGabrielle Alves, Dietmar Jannach, Luan Soares de Souza, Marcelo Garcia Manzato. 684-689 [doi]
• TreatRAG: A Framework for Personalized Treatment RecommendationChao Chin Liu, Hao-Ren Yao, Der-Chen Chang, Ophir Frieder. 690-695 [doi]
• A Reproducibility Study of Product-side Fairness in Bundle RecommendationHuy-Son Nguyen, Yuanna Liu, Masoud Mansoury, Mohammad Aliannejadi, Alan Hanjalic, Maarten de Rijke. 696-705 [doi]
• Are We Really Making Recommendations Robust? Revisiting Model Evaluation for Denoising RecommendationGuohang Zeng, Jie Lu 0001, Guangquan Zhang 0001. 706-715 [doi]
• Context Trails: A Dataset to Study Contextual and Route RecommendationPablo Sánchez, Alejandro Bellogín, Jose L. Jorro-Aragoneses. 716-725 [doi]
• DistillRecDial: A Knowledge-Distilled Dataset Capturing User Diversity in Conversational RecommendationAlessandro Francesco Maria Martina, Alessandro Petruzzelli, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. 726-735 [doi]
• Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline EvaluationPedro R. Pires, Gregório F. Azevedo, Pietro L. Campos, Rafael T. Sereicikas, Tiago A. Almeida 0001. 736-745 [doi]
• Exploring the Potential of LLMs for Serendipity Evaluation in Recommender SystemsLi Kang, Yuhan Zhao, Li Chen. 746-754 [doi]
• Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant ItemsMaria Vlachou. 755-763 [doi]
• GreenFoodLens: Sustainability Labels for Food RecommendationGiacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras, Giacomo Medda, Giovanni Murgia. 764-773 [doi]
• How Powerful are LLMs to Support Multimodal Recommendation? A Reproducibility Study of LLMRecMaria Lucia Fioretti, Nicola Laterza, Alessia Preziosa, Daniele Malitesta, Claudio Pomo, Fedelucio Narducci, Tommaso Di Noia. 774-782 [doi]
• Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility StudyRobin Ungruh, Alejandro Bellogín, Dominik Kowald, Maria Soledad Pera. 783-791 [doi]
• Informfully Recommenders - Reproducibility Framework for Diversity-aware Intra-session RecommendationsLucien Heitz, Runze Li, Oana Inel, Abraham Bernstein. 792-801 [doi]
• Model Meets Knowledge: Analyzing Knowledge Types for Conversational Recommender SystemsJujia Zhao, Yumeng Wang, Zhaochun Ren, Suzan Verberne. 802-811 [doi]
• Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack PerspectiveYubo Wang, Min Tang, Nuo Shen, Shujie Cui, Weiqing Wang. 812-821 [doi]
• Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"Dominykas Seputis, Yongkang Li, Karsten Langerak, Serghei Mihailov. 822-831 [doi]
• Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized RecommendationGenki Kusano, Kosuke Akimoto, Kunihiro Takeoka. 832-841 [doi]
• Revisiting the Performance of Graph Neural Networks for Session-based RecommendationFaisal Shehzad, Dietmar Jannach. 842-846 [doi]
• See the Movie, Hear the Song, Read the Book: Extending MovieLens-1M, Last.fm-2K, and DBbook with Multimodal DataGiuseppe Spillo, Elio Musacchio, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. 847-856 [doi]
• The XITE Million Sessions DatasetRalvi Isufaj, Ruslan Tsygankov, Zoltán Szlávik. 857-864 [doi]
• TIM-Rec: Explicit Sparse Feedback on Multi-Item Upselling Recommendations in an Industrial Dataset of Telco CallsAlessandro Sbandi, Federico Siciliano, Fabrizio Silvestri. 865-873 [doi]
• Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential RecommendersDanil Gusak, Anna Volodkevich, Anton Klenitskiy, Alexey Vasilev, Evgeny Frolov. 874-883 [doi]
• "We Share Our Code Online": Why This Is Not Enough to Ensure Reproducibility and Progress in Recommender Systems ResearchFaisal Shehzad, Timo Breuer 0002, Maria Maistro, Dietmar Jannach. 884-893 [doi]
• Yambda-5B - A Large-Scale Multi-Modal Dataset for Ranking and RetrievalAlexander Ploshkin, Vladislav Tytskiy, Alexey Pismenny, Vladimir Baikalov, Evgeny Taychinov, Artem Permiakov, Daniil Burlakov, Eugene Krofto. 894-901 [doi]
• A Media Content Recommendation Method for Playlist Curators using LLM-Based Query ExpansionYuta Hagio, Chigusa Yamamura, Hiromu Ogawa, Hisayuki Ohmata, Arisa Fujii. 902-906 [doi]
• Agentic Personalisation of Cross-Channel Marketing ExperiencesSami Abboud, Eleanor Hanna, Olivier Jeunen, Vineesha Raheja, Schaun Wheeler. 907-910 [doi]
• An Analysis of Learned Product Embeddings in an E-Commerce ContextMate Hartstein, Eva Giannatou, Martin Tegner. 911-914 [doi]
• Balanced Public Service Media Recommendation Trade-offs with a Light Carbon FootprintMarcel Hauck, Michael Huber, Juri Diels, David Wittenberg, Dietmar Jannach. 915-918 [doi]
• Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation UpdatesChangping Meng, Hongyi Ling, Jianling Wang, Yifan Liu, Shuzhou Zhang, Dapeng Hong, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Haokai Lu, Ningren Han. 919-922 [doi]
• Closing the Online-Offline Gap: A Scalable Framework for Composed Model EvaluationMahanth Kumar Beeraka, Chen Chen, Yining Lu, Briac Marcatte, Weikun Lyu, Brooke Bian, Enriko Aryanto, Ellie Wen, Mohamed A. Radwan, Tianshan Cui, Wenjing Lu, Mohsen Malmir, Yang Li. 923-926 [doi]
• Cold Starting a New Content Type: A Case Study with Netflix LiveYunan Hu, Mark Thornburg, Mario García-Armas, Vito Ostuni, Anne Cocos, Kriti Kohli, Christoph Kofler, Rob Saltiel. 927-930 [doi]
• Contrastive Conditional Embeddings for Item-based Recommendation at E-commerce ScaleAkira Fukumoto, Aghiles Salah, Sarthak Shrivastava, Alexandru Tatar, Yannick Schwartz, Vincent Michel, Lee Xiong. 931-934 [doi]
• Cross-Batch Aggregation for Streaming Learning from Label Proportions in Industrial-Scale Recommendation SystemsJonathan Valverde, Tiansheng Yao, Xiang Li, Yuan Gao, Yin Zhang 0011, Andrew Evdokimov, Adam Kraft, Samuel Ieong, Jerry Zhang, Ed H. Chi, Derek Zhiyuan Cheng, Ruoxi Wang. 935-939 [doi]
• Decoupled Entity Representation Learning for Pinterest Ads RankingJie Liu, Yinrui Li, Jiankai Sun, Kungang Li, Han Sun, Sihan Wang, Huasen Wu, Siyuan Gao, Paulo Soares, Nan Li, Zhifang Liu, Haoyang Li, Siping Ji, Ling Leng, Prathibha Deshikachar. 940-944 [doi]
• Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at PinterestXiao Yang, Mehdi Ayed, Longyu Zhao, Fan Zhou, Yuchen Shen, Abe Engle, Jinfeng Zhuang, Ling Leng, Jiajing Xu, Charles Rosenberg, Prathibha Deshikachar. 945-948 [doi]
• Efficient Off-Policy Evaluation of Content Blending in Station-Based Music ExperiencesChelsea Weaver, Arvind Balasubramanian, Juan Borgnino, Ben London 0001. 949-953 [doi]
• Enhancing Embedding Representation Stability in Recommendation Systems with Semantic IDCarolina Zheng, Minhui Huang, Dmitrii Pedchenko, Kaushik Rangadurai, Siyu Wang, Fan Xia, Gaby Nahum, Jie Lei, Yang Yang 0083, Tao Liu 0035, Zutian Luo, Xiaohan Wei, Dinesh Ramasamy, Jiyan Yang, Yiping Han, Lin Yang, Hangjun Xu, Rong Jin 0001, Shuang Yang. 954-957 [doi]
• Enhancing Online Ranking Systems via Multi-Surface Co-Training for Content UnderstandingGwendolyn Zhao, Yilin Zheng, Raghu Keshavan, Lukasz Heldt, Qian Sun, Fabio Soldo, Li Wei, Aniruddh Nath, Nikhil Khani, Weilong Yang, Dapo Omidiran, Rein Zhang, Mei Chen, Lichan Hong, Xinyang Yi. 958-961 [doi]
• Generalized User Representations for Large-Scale Recommendations and Downstream TasksGhazal Fazelnia, Sanket Gupta, Claire Keum, Mark Koh, Timothy Christopher Heath, Guillermo Carrasco Hernández, Stephen Xie, Nandini Singh, Ian Anderson 0003, Maya Hristakeva, Petter Pehrson Skidén, Mounia Lalmas. 962-966 [doi]
• Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender SystemsTimo Wilm, Philipp Normann. 967-970 [doi]
• Improve the Personalization of Large-Scale Ranking Systems by Integrating User Survey FeedbackMengxi Lv, Drew Hogg, Thomas Grubb, Shashank Bassi, Min Li, Cayman Simpson, Senthil Rajagopalan. 971-974 [doi]
• Improving Visual Recommendation on E-commerce Platforms Using Vision-Language ModelsYuki Yada, Sho Akiyama, Ryo Watanabe, Yuta Ueno, Yusuke Shido, Andre Rusli. 975-978 [doi]
• In-context Learning for Addressing User Cold-start in Sequential Movie RecommendersXurong Liang, Vu Nguyen, Vuong Le, Paul Albert, Julien Monteil. 979-982 [doi]
• Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-RankYunus Lutz, Timo Wilm, Philipp Duwe. 983-986 [doi]
• Item-centric Exploration for Cold Start ProblemDong Wang, Junyi Jiao, Arnab Bhadury, Yaping Zhang, Mingyan Gao, Onkar Dalal. 987-990 [doi]
• Kamae: Bridging Spark and Keras for Seamless ML PreprocessingGeorge Barrowclough, Marian Andrecki, James Shinner, Daniele Donghi. 991-994 [doi]
• LADDER: LLM-Annotated Data for Dogfooded Evaluation of RankingsMattia Ottoborgo. 995-998 [doi]
• Leveraging Explicit Negative Feedback in Large-Scale Recommendation Systems: A Case StudyMadhura Raju, Manisha Sharma, Hongyu Xiong, Bingfeng Deng, Meng Na. 999-1001 [doi]
• LLM-Powered Nuanced Video Attribute Annotation for Enhanced RecommendationsBoyuan Long, Yueqi Wang, Hiloni Mehta, Mick Zomnir, Omkar Pathak, Changping Meng, Ruolin Jia, Yajun Peng, Dapeng Hong, Xia Wu, Mingyan Gao, Onkar Dalal, Ningren Han. 1002-1005 [doi]
• Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing SearchIvo Silva, Guilherme G. Bonaldo, Pedro F. Nogueira. 1006-1009 [doi]
• Metadata Generation and Evaluation using LLMs - Case Study on Canonical TitlesSinan Zhu, Sanja Simonovikj, Darren Edmonds, Yang Sun. 1010-1013 [doi]
• Minimize Negative Experiences in Video Recommendation Systems with Multimodal Large Language ModelsSuman Malani, Youwei Zhang, Liang Liu 0017. 1014-1016 [doi]
• Never Miss an Episode: How LLMs are Powering Serial Content Discovery on YouTubeAditee Kumthekar, Li Wei, Andrea Bettale, Mahesh Sathiamoorthy, Zrinka Puljiz, Aditya Mahajan. 1017-1021 [doi]
• Not All Impressions Are Created Equal: Psychology-Informed Retention Optimization for Short-Form Video RecommendationYuyan Wang, Jing Zhong, Yuxin Cui, Zhaohui Guo, Chuanqi Wei, Yanchen Wang, Zellux Wang. 1022-1025 [doi]
• Operational Twin-Driven AI Recommender for Strategic Service PlanningVivek Singh, Sarith Mohan, Chetan Srinidhi, Santosh Pai, Ullaskrishnan Poikavila, Codruta Ene, Ankur Kapoor, Neil Biehn, Dorin Comaniciu. 1026-1029 [doi]
• Orthogonal Low Rank Embedding StabilizationKevin Zielnicki, Ko-Jen Hsiao. 1030-1033 [doi]
• Pareto-Optimal Solution: Optimizing Engagement and RevenueShaghayegh Agah, Shaun Schaeffer, Maria Peifer, Neeraj Sharma, Ankit Maheshwari, Sardar Hamidian. 1034-1037 [doi]
• Personalized Interest Graphs for Theme-Driven User BehaviorOded Zinman, Nazmul Chowdhury, Leandro Fiaschetti, Yuri M. Brovman, Guy Feigenblat, Yotam Eshel. 1038-1041 [doi]
• Practical Multi-Task Learning for Rare Conversions in Ad TechYuval Dishi, Ophir Friedler, Yonatan Karni, Natalia Silberstein, Yulia Stolin. 1042-1045 [doi]
• RADAR: Recall Augmentation through Deferred Asynchronous RetrievalAmit Jaspal, Qian Dang, Ajantha Ramineni. 1046-1049 [doi]
• RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain RecommendationRenzhi Wu, Junjie Yang, Li Chen 0028, Hong Li, Li Yu, Hong Yan. 1050-1053 [doi]
• SASRec in Action: Real-World Adaptations for ZDF Streaming ServiceVenkata Harshit Koneru, Xenija Neufeld, Sebastian Loth, Andreas Grün. 1054-1057 [doi]
• Scaling Generative Recommendations with Context Parallelism on Hierarchical Sequential TransducersYue Dong, Han Li, Shen Li, Nikhil Patel, Xing Liu, Xiaodong Wang, Chuanhao Zhuge. 1058-1061 [doi]
• Scaling Image Variant Optimization Through Customer Bucketing and Response Caching: A Large-Scale Implementation at Amazon Prime VideoHaiyun Jin, Bobby Patel. 1062-1065 [doi]
• Scaling Retrieval for Web-Scale Recommenders: Lessons from Inverted Indexes to Embedding SearchYuchin Juan, Jianqiang Shen, Shaobo Zhang, Qianqi Shen, Caleb Johnson, Luke Simon, Liangjie Hong, Wenjing Zhang. 1066-1069 [doi]
• Semantic IDs for Music RecommendationM. Jeffrey Mei, Florian Henkel, Samuel E. Sandberg, Oliver Bembom, Andreas F. Ehmann. 1070-1073 [doi]
• SEMORec: A Scalarized Efficient Multi-Objective Recommendation FrameworkSofia Maria Nikolakaki, Siyong Ma, Srivas Chennu, Humeyra Topcu-Altintas. 1074-1077 [doi]
• Simulating Discoverability for Upcoming Content in TV Entertainment PlatformsAdeep Hande, Kishorekumar Sundararajan, Yidnekachew Endale, Sardar Hamidian. 1078-1081 [doi]
• SocRipple: A Two-Stage Framework for Cold-Start Video RecommendationsAmit Jaspal, Kapil Dalwani, Ajantha Ramineni. 1082-1085 [doi]
• Stream Normalization for CTR PredictionYizhou Sang, Congcong Liu, Yuying Chen, Zhiwei Fang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law, JingPing Shao. 1086-1090 [doi]
• Streaming Trends: A Low-Latency Platform for Dynamic Video Grouping and Trending Corpora BuildingYang Gu, Caroline Zhou, Qiao Zhang, Scott Wang, Yongzhe Wang, Li Zhang, Nikos Parotsidis, CJ Carey, Ashkan Fard, Mingyan Gao, Yaping Zhang, Sourabh Bansod. 1091-1094 [doi]
• Suggest, Complement, Inspire: Story of Two-Tower Recommendations at Allegro.comAleksandra Maria Osowska-Kurczab, Klaudia Nazarko, Mateusz Marzec, Lidia Wojciechowska, Eliska Kremenová. 1095-1098 [doi]
• The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender SystemsPetr Kasalický, Martin Spisák, Vojtech Vancura, Daniel Bohunek, Rodrigo Alves, Pavel Kordík. 1099-1103 [doi]
• Unified Survey Modeling to Limit Negative User Experiences in Recommendation SystemsChengHui Yu, Haoze Wu, Jian Ding, Bingfeng Deng, Hongyu Xiong. 1104-1107 [doi]
• USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage RecommendationsJiaqi Zheng 0015, Cheng Guo, Yi Cao, Chaoqun Hou, Tong Liu, Bo Zheng. 1108-1111 [doi]
• User Long-Term Multi-Interest Retrieval Model for RecommendationYue Meng, Cheng Guo, Xiaohui Hu, Honghu Deng, Yi Cao, Tong Liu, Bo Zheng. 1112-1116 [doi]
• You Say Search, I Say Recs: A Scalable Agentic Approach to Query Understanding and Exploratory Search at SpotifyEnrico Palumbo, Marcus Isaksson, Alexandre Tamborrino, Maria Movin, Catalin Dincu, Ali Vardasbi, Lev Nikeshkin, Oksana Gorobets, Anders Nyman, Poppy Newdick, Hugues Bouchard, Paul N. Bennett, Mounia Lalmas, Dani Doro, Christine Doig Cardet, Ziad Sultan. 1117-1121 [doi]
• Zero-shot Cross-domain Knowledge Distillation: A Case study on YouTube MusicSrivaths Ranganathan, Chieh Lo, Bernardo Cunha, Nikhil Khani, Li Wei, Aniruddh Nath, Shawn Andrews, Gergo Varady, Yanwei Song, Jochen Klingenhoefer, Tim Steele. 1122-1125 [doi]
• A Dual-Key Attention Framework for Sequential Recommendation with Side InformationMinje Kim, Wooseung Kang, Gun-Woo Kim, Chie Hoon Song, Suwon Lee, Sang-Min Choi. 1126-1131 [doi]
• Addressing Multiple Hypothesis Bias in CTR Prediction for Ad SelectionOren Sar Shalom, Neil Daftary. 1132-1136 [doi]
• Balancing Accuracy and Novelty with Sub-Item PopularityChiara Mallamaci, Aleksandr V. Petrov, Alberto Carlo Maria Mancino, Vito Walter Anelli, Tommaso Di Noia, Craig Macdonald. 1137-1141 [doi]
• Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential PatternsVeronika Ivanova, Evgeny Frolov, Alexey Vasilev. 1142-1147 [doi]
• Beyond Clicks: Eye-Tracking Insights into User Responses to Different Recommendation TypesGeorgios Koutroumpas, Matteo Mazzini, Sebastian Idesis, Mireia Masias Bruns, Joemon M. Jose, Sergi Abadal, Ioannis Arapakis. 1148-1152 [doi]
• Debiasing Implicit Feedback Recommenders via Sliced Wasserstein Distance-based RegularizationGustavo Escobedo, David Penz, Markus Schedl. 1153-1158 [doi]
• Describe What You See with Multimodal Large Language Models to Enhance Video RecommendationsMarco De Nadai, Andreas Damianou, Mounia Lalmas. 1159-1163 [doi]
• Don't Get Ahead of Yourself: A Critical Study on Data Leakage in Offline Evaluation of Sequential RecommendersHuy-Hoang Le, Yang Liu, Alan Medlar, Dorota Glowacka. 1164-1168 [doi]
• End-to-End Time Interval-wise Segmentation for Sequential RecommendationMinje Kim, Wooseung Kang, Gun-Woo Kim, Chie Hoon Song, Suwon Lee, Sang-Min Choi. 1169-1174 [doi]
• eSASRec: Enhancing Transformer-based Recommendations in a Modular FashionDaria Tikhonovich, Nikita Zelinskiy, Aleksandr V. Petrov, Mayya Spirina, Andrei Semenov, Andrey V. Savchenko, Sergei Kuliev. 1175-1180 [doi]
• Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-JudgeFrancesco Fabbri, Gustavo Penha, Edoardo D'Amico, Alice Wang, Marco De Nadai, Jackie Doremus, Paul Gigioli, Andreas Damianou, Oskar Stål, Mounia Lalmas. 1181-1186 [doi]
• Fine-tuning for Inference-efficient Calibrated RecommendationsOleg Lesota, Adrian Bajko, Max Walder, Matthias Wenzel, Antonela Tommasel, Markus Schedl. 1187-1192 [doi]
• From Previous Plays to Long-Term Tastes: Exploring the Long-term Reliability of Recommender Systems Simulations for ChildrenRobin Ungruh, Alejandro Bellogín, Maria Soledad Pera. 1193-1198 [doi]
• How Fair is Your Diffusion Recommender Model?Daniele Malitesta, Giacomo Medda, Erasmo Purificato, Mirko Marras, Fragkiskos D. Malliaros, Ludovico Boratto. 1199-1205 [doi]
• Investigating Carbon Footprint of Recommender Systems Beyond Training TimeJosef Schodl, Oleg Lesota, Antonela Tommasel, Markus Schedl. 1206-1211 [doi]
• Learning geometry-aware recommender systems with manifold regularizationZaira Zainulabidova, Julia Borisova, Alexander Hvatov. 1212-1216 [doi]
• Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved RecommendationsViacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov. 1217-1221 [doi]
• Lift It Up Right: A Recommender System for Safer Lifting PosturesGaetano Dibenedetto, Pasquale Lops, Marco Polignano, Helma Torkamaan. 1222-1227 [doi]
• Meta Off-Policy EstimationOlivier Jeunen. 1228-1233 [doi]
• Mitigating Popularity Bias in Counterfactual Explanations using Large Language ModelsArjan Hasami, Masoud Mansoury. 1234-1239 [doi]
• Normative Alignment of Recommender Systems via Internal Label ShiftJohannes Kruse 0002, Kasper Lindskow, Michael Riis Andersen, Ryotaro Shimizu, Julian J. McAuley, Pierre-Alexandre Mattei, Jes Frellsen. 1240-1245 [doi]
• Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender SystemsParviz Ahmadov, Masoud Mansoury. 1246-1250 [doi]
• PAIRSAT: Integrating Preference-Based Signals for User Satisfaction Estimation in Dialogue SystemsEran Fainman, Adir Solomon, Osnat Mokryn. 1251-1255 [doi]
• Parameter-Efficient Single Collaborative Branch for RecommendationMarta Moscati, Shah Nawaz, Markus Schedl. 1256-1260 [doi]
• Probabilistic Modeling, Learnability and Uncertainty Estimation for Interaction Prediction in Movie Rating DatasetsJennifer Poernomo, Nicole Gabrielle Lee Tan, Rodrigo Alves, Antoine Ledent. 1261-1266 [doi]
• Recommendation Is a Dish Better Served WarmDanil Gusak, Nikita Sukhorukov, Evgeny Frolov. 1267-1272 [doi]
• Recurrent Autoregressive Linear Model for Next-Basket RecommendationTereza Zmeskalová, Antoine Ledent, Martin Spisák, Pavel Kordík, Rodrigo Alves. 1273-1278 [doi]
• Rethinking Subjective Features in Recommender Systems: Personal Views Over Aggregated ValuesArsen Matej Golubovikj, Marko Tkalcic. 1279-1283 [doi]
• RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial GraphsZhongtian Sun, Anoushka Harit. 1284-1289 [doi]
• SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group RecommendationsVit Kostejn, Ladislav Peska, Martin Spisák. 1290-1295 [doi]
• Semantic IDs for Joint Generative Search and RecommendationGustavo Penha, Edoardo D'Amico, Marco De Nadai, Enrico Palumbo, Alexandre Tamborrino, Ali Vardasbi, Max Lefarov, Shawn Lin, Timothy Christopher Heath, Francesco Fabbri, Hugues Bouchard. 1296-1301 [doi]
• SlateLLM: Distilling LLM Semantics into Session-Aware Slate Recommendation without Inference OverheadAayush Singha Roy, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley. 1302-1306 [doi]
• t-Testing the Waters: Empirically Validating Assumptions for Reliable A/B-TestingOlivier Jeunen. 1307-1310 [doi]
• The Hidden Cost of Defaults in Recommender System EvaluationHannah Berling, Robin Svahn, Alan Said. 1311-1316 [doi]
• Unobserved Negative Items in Recommender Systems: Challenges and Solutions for Evaluation and LearningMasahiro Sato. 1317-1321 [doi]
• APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset SelectionTobias Vente, Michael Heep, Abdullah Abbas, Theodor Sperle, Joeran Beel, Bart Goethals. 1322-1324 [doi]
• ArtAICare: An End-to-End Platform for Personalized Art TherapyBereket Abera Yilma, Saravanakumar Duraisamy, Stefan Penchev, Tudor Pristav, Luis A. Leiva. 1325-1327 [doi]
• ArtEx: A User-Controllable Web Interface for Visual Art RecommendationsRully Agus Hendrawan, Peter Brusilovsky, Luis A. Leiva, Bereket Abera Yilma. 1328-1330 [doi]
• Blooming Beats: An Interactive Music Recommender System Grounded in TRACE Principles and Data HumanismIbrahim Al Hazwani, Daniel Lutziger, Carlos Kirchdorfer, Luca Huber, Oliver Robin Aschwanden, Jürgen Bernard, Ludovico Boratto. 1331-1333 [doi]
• Flights Pricelock Fee Recommendation on Online Travel Agent PlatformAkash Khetan, Narasimha Medeme, Deepak Yadav, Anmol Porwal. 1334-1336 [doi]
• Interactive Playlist Generation from TitlesEléa Vellard, Enzo Charolois-Pasqua, Youssra Rebboud, Pasquale Lisena, Raphaël Troncy. 1337-1339 [doi]
• Large Language Model-based Recommendation System AgentsTommaso Carraro, Brijraj Singh, Niranjan Pedanekar. 1340-1342 [doi]
• PRISM: From Individual Preferences to Group Consensus through Conversational AI-Mediated and Visual ExplanationsIbrahim Al Hazwani, Oliver Robin Aschwanden, Oana Inel, Jürgen Bernard, Ludovico Boratto. 1343-1345 [doi]
• RecViz: Intuitive Graph-based Visual Analytics for Dataset Exploration and Recommender System EvaluationJackson Dam, Zixuan Yi, Iadh Ounis. 1346-1348 [doi]
• Travel Together, Play Together: Gamifying a Group Recommender System for TourismPatrícia Alves, Joana Neto, Jorge Lima, José Silva 0003, Luís Conceição, Goreti Marreiros. 1349-1352 [doi]
• VisualReF: Interactive Image Search Prototype with Visual Relevance FeedbackBulat Khaertdinov, Mirela Popa, Nava Tintarev. 1353-1356 [doi]
• 12th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'25)Peter Brusilovsky, Alexander Felfernig, Pasquale Lops, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen. 1357-1359 [doi]
• Beyond Algorithms: Reclaiming the Interdisciplinary Roots of Recommender Systems (BEYOND 2025)Eva Zangerle, Alan Said, Christine Bauer 0001. 1360-1361 [doi]
• CONSEQUENCES 2025 - The 4th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender SystemsHarrie Oosterhuis, Olivier Jeunen, Yuta Saito, Yixin Wang 0002, Flavian Vasile, Thorsten Joachims. 1362-1365 [doi]
• EARL: The 2nd Workshop on Evaluating and Applying Recommender Systems with Large Language ModelsIrene Li, Ruihai Dong, Guillaume Salha-Galvan, Aonghus Lawlor, Dairui Liu, Lei Li. 1366-1370 [doi]
• FAccTRec 2025: The 8th Workshop on Responsible RecommendationMichael D. Ekstrand, Toshihiro Kamishima, Amifa Raj, Karlijn Dinnissen. 1371-1372 [doi]
• Fifth Workshop on Recommender Systems for Human Resources (RecSys in HR 2025)Toine Bogers, Mesut Kaya, Jens-Joris Decorte, Chris Johnson 0011, Guillaume Bied. 1373-1377 [doi]
• First International Workshop on Data Quality-Aware Multimodal Recommendation (DaQuaMRec)Claudio Pomo, Dietmar Jannach, Yubin Kim 0001, Daniele Malitesta, Alberto Carlo Maria Mancino, Julian J. McAuley, Alessandro B. Melchiorre, Shah Nawaz. 1378-1382 [doi]
• MuRS: 3rd Music Recommender Systems WorkshopAndres Ferraro, Lorenzo Porcaro, Christine Bauer 0001. 1383-1385 [doi]
• NORMalize 2025: The Third Workshop on Normative Design and Evaluation of Recommender SystemsLien Michiels, Sanne Vrijenhoek, Alain D. Starke, Johannes Kruse 0002, Savvina Daniil. 1386-1388 [doi]
• RecSys Challenge 2025: Universal Behavioral Profiles for Recommender SystemsJacek Dabrowski 0004, Maria Janicka, Lukasz Sienkiewicz, Gergely Stomfai, Dietmar Jannach, Francesco Barile, Marco Polignano, Claudio Pomo, Abhishek Srivastava 0004. 1389-1393 [doi]
• Second International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2025)Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, Francesca Maridina Malloci, Noemi Mauro, Francesco Ricci 0001. 1394-1398 [doi]
• The 13th International Workshop on News Recommendation and Analytics (INRA 2025)Andreea Iana, Célina Treuillier, Vandana Yadav, Benjamin Kille, Andreas Lommatzsch, Özlem Özgöbek. 1399-1403 [doi]
• The Second Workshop on Generative AI for E-commerceMansi Ranjit Mane, Djordje Gligorijevic, Dingxian Wang, Topojoy Biswas, Evren Körpeoglu, Marios Savvides, Yongfeng Zhang, Julian J. McAuley. 1404-1408 [doi]
• Workshop on Context-Aware Recommender SystemsGediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger. 1409-1411 [doi]
• Workshop on Recommenders in Tourism (RecTour) 2025Julia Neidhardt, Tsvi Kuflik, Amit Livne, Markus Zanker, Wolfgang Wörndl. 1412-1413 [doi]
• A Hands-on Dive Into Quantum Computing for Recommender SystemsMaurizio Ferrari Dacrema, Paolo Cremonesi. 1414-1416 [doi]
• A Tutorial on Agentic LLM for Recommender SystemsChengkai Huang, Junda Wu, Tong Yu 0001, Julian J. McAuley, Lina Yao 0001. 1417-1419 [doi]
• A Tutorial on Recent Advances in Generative Conversational Recommender SystemsThomas Elmar Kolb, Ahmadou Wagne, Ashmi Banerjee, Fatemeh Nazary, Julia Neidhardt, Yashar Deldjoo, Tommaso Di Noia. 1420-1422 [doi]
• concept2code: Sequential Recommendation with Large Language ModelsOmprakash Sonie. 1423-1424 [doi]
• Data Access for Recommender Systems Research: leveraging the EU's Digital Services ActJoão Vinagre, Lorenzo Porcaro, Silvia Merisio, Erasmo Purificato, Emilia Gómez. 1425-1426 [doi]
• Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications - An Industrial TutorialReza Yousefi Maragheh, Yashar Deldjoo, Chi Wang, Jason Cho 0001, Derek Cheng. 1427-1429 [doi]
• Multi-Armed Bandits in the WildKim Falk. 1430-1431 [doi]
• Standard Practices for Data Processing and Multimodal Feature Extraction in Recommendation with DataRec and Ducho (D&D4Rec)Alberto Carlo Maria Mancino, Matteo Attimonelli, Angela Di Fazio, Daniele Malitesta, Tommaso Di Noia. 1432-1434 [doi]
• Adding Value to Low-Resource Industrial Recommender SystemsCornelia M. Kloppers. 1435-1438 [doi]
• Addressing Multi-stakeholder Fairness Concerns in Recommender Systems Through Social ChoiceAmanda Aird. 1439-1444 [doi]
• Advancing User-Centric Evaluation and Design of Conversational Recommender SystemsMichael Müller. 1445-1450 [doi]
• Are Recommender Systems Serving Children? Toward Child-Aware Design and EvaluationRobin Ungruh. 1451-1457 [doi]
• Bayesian Perspectives on Offline Evaluation for Recommender SystemsMichael Benigni. 1458-1462 [doi]
• Beyond Persuasion: Adaptive Warnings and Balanced Explanations for Informed Decision-Making in Recommender SystemsElaheh Jafari. 1463-1468 [doi]
• Challenges in Perfume Recommender Systems: Navigating Subjectivity, Context and Sensory DataElena-Ruxandra Lutan. 1469-1472 [doi]
• Fair and Transparent Recommender Systems for AdvertisementsDina Zilbershtein. 1473-1478 [doi]
• Full-Page Recommender: A Modular Framework for Multi-Carousel RecommendationsJan Kislinger. 1479-1484 [doi]
• Narrative-Driven Itinerary Recommendation: LLM Integration for Immersive Urban WalkingFabio Ferrero. 1485-1491 [doi]
• Personalized Image Generation for Recommendations Beyond CatalogsGabriel Alfonso Patron. 1492-1498 [doi]
• Recommender Systems for Digital Humanities and Archives: Multistakeholder Evaluation, Scholarly Information Needs, and Multimodal SimilarityFlorian Atzenhofer-Baumgartner. 1499-1505 [doi]