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ORCIDRecSys 2025: Prague, Czech Republic
- Mária Bieliková, Pavel Kordík, Markus Schedl, Marco de Gemmis, Sole Pera, Rodrigo Alves, Olivier Jeunen, Vito Ostuni:
Proceedings of the Nineteenth ACM Conference on Recommender Systems, RecSys 2025, Prague, Czech Republic, September 22-26, 2025. ACM 2025, ISBN 979-8-4007-1364-4
Full papers
- Enzo Charolois-Pasqua
, Eléa Vellard, Youssra Rebboud, Pasquale Lisena, Raphaël Troncy:
A Language Model-Based Playlist Generation Recommender System. 1-11 - Junrui Liu, Tong Li, Mingliang Yu, Shiqiu Yang, Zifang Tang, Zhen Yang:
A Multi-Factor Collaborative Prediction for Review-based Recommendation. 12-20 - Thorsten Krause, Harrie Oosterhuis:
A Non-Parametric Choice Model That Learns How Users Choose Between Recommended Options. 21-30 - Bereket Abera Yilma, Luis A. Leiva:
Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation. 31-40 - Haruka Kiyohara, Daniel Yiming Cao, Yuta Saito, Thorsten Joachims:
An Off-Policy Learning Approach for Steering Sentence Generation towards Personalization. 41-50 - Matteo Fabbri, Ludovico Boratto:
Auditing Recommender Systems for User Empowerment in Very Large Online Platforms under the Digital Services Act. 51-61 - Haotian Jiang, Sibendu Paul, Haiyang Zhang, Caren Chen:
Beyond Immediate Click: Engagement-Aware and MoE-Enhanced Transformers for Sequential Movie Recommendation. 62-71 - Ruoyu Li, Yangtao Zhou, Chenzhang Li, Hua Chu, Jianan Li, Yuhan Bian:
Breaking Knowledge Boundaries: Cognitive Distillation-enhanced Cross-Behavior Course Recommendation Model. 72-81 - Chang Meng, Chenhao Zhai, Xueliang Wang, Shuchang Liu, Xiaoqiang Feng, Lantao Hu, Xiu Li, Han Li, Kun Gai:
Enhancing Online Video Recommendation via a Coarse-to-fine Dynamic Uplift Modeling Framework. 82-92 - Bowen Zheng, Zihan Lin, Enze Liu, Chen Yang, Enyang Bai, Cheng Ling, Han Li, Wayne Xin Zhao, Ji-Rong Wen:
Enhancing Sequential Recommender with Large Language Models for Joint Video and Comment Recommendation. 93-103 - Yuhan Wang, Qing Xie, Zhifeng Bao, Mengzi Tang, Lin Li, Yongjian Liu:
Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement. 104-113 - Weijiang Lai, Beihong Jin, Jiongyan Zhang, Yiyuan Zheng, Jian Dong, Jia Cheng, Jun Lei, Xingxing Wang:
Exploring Scaling Laws of CTR Model for Online Performance Improvement. 114-123 - Teng Shi, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song, Enyun Yu:
GenSAR: Unifying Balanced Search and Recommendation with Generative Retrieval. 124-134 - Luyi 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:
GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization. 135-144 - Honghui Bao, Wenjie Wang, Xinyu Lin, Fengbin Zhu, Teng Sun, Fuli Feng, Tat-Seng Chua:
Heterogeneous User Modeling for LLM-based Recommendation. 145-154 - Hengyu Zhang, Chunxu Shen, Xiangguo Sun, Jie Tan, Yanchao Tan, Yu Rong, Hong Cheng, Lingling Yi:
Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation. 155-164 - Patrik Dokoupil, Ludovico Boratto, Ladislav Peska:
How Do Users Perceive Recommender Systems' Objectives? 165-176 - Amanda Aird, Elena Stefancova, Anas Buhayh, Cassidy All, Martin Homola, Nicholas Mattei, Robin Burke:
Integrating Individual and Group Fairness for Recommender Systems through Social Choice. 177-186 - Youlin Wu, Yuanyuan Sun, Xiaokun Zhang, Haoxi Zhan, Bo Xu, Liang Yang, Hongfei Lin:
IP2: Entity-Guided Interest Probing for Personalized News Recommendation. 187-196 - Yuyue Zhao, Jin Huang, Shuchang Liu, Jiancan Wu, Xiang Wang, Maarten de Rijke:
LANCE: Exploration and Reflection for LLM-based Textual Attacks on News Recommender Systems. 197-206 - Yue Chen, Susen Yang, Tong Zhang, Chao Wang, Mingyue Cheng, Chenyi Lei, Han Li:
Lasso: Large Language Model-based User Simulator for Cross-Domain Recommendation. 207-216 - Chaoyi Jiang, Abdulla Alshabanah, Murali Annavaram:
LEAF: Lightweight, Efficient, Adaptive and Flexible Embedding for Large-Scale Recommendation Models. 217-225 - Weixin Chen, Yuhan Zhao, Li Chen, Weike Pan:
Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users. 226-236 - Yunzhe Li, Junting Wang, Hari Sundaram, Zhining Liu:
LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation. 237-246 - Zheng 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:
LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders. 247-256 - Mesut Kaya, Toine Bogers:
Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring. 257-267 - Yutong Li, Xinyi Zhang:
MDSBR: Multimodal Denoising for Session-based Recommendation. 268-278 - Haocheng Dou, Tao Lian, Xin Xin:
Measuring Interaction-Level Unlearning Difficulty for Collaborative Filtering. 279-288 - Weijiang Lai, Beihong Jin, Yapeng Zhang, Yiyuan Zheng, Rui Zhao, Jian Dong, Jun Lei, Xingxing Wang:
Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction. 289-298 - Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen, Xiao Zhang, Ming He, Jianping Fan, Jun Xu:
MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation. 299-308 - Xu Zhao, Ruibo Ma, Jiaqi Chen, Weiqi Zhao, Ping Yang, Yao Hu:
Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network. 309-318 - Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Hewei Wang, Wei Wang, Xiping Hu, Edith C. H. Ngai:
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation. 319-329 - Zhongyu Ouyang, Mingxuan Ju, Soroush Vosoughi, Yanfang Ye:
Non-parametric Graph Convolution for Re-ranking in Recommendation Systems. 330-339 - Yudai Hayashi, Shuhei Goda, Yuta Saito:
Off-Policy Evaluation and Learning for Matching Markets. 340-349 - Peiyao Wang, Zhan Shi, Amina Shabbeer, Ben London:
Off-Policy Evaluation of Candidate Generators in Two-Stage Recommender Systems. 350-359 - Bruno L. Pereira, Alan Said, Rodrygo L. T. Santos:
On the Reliability of Sampling Strategies in Offline Recommender Evaluation. 360-369 - Chenglei Shen, Jiahao Zhao, Xiao Zhang, Weijie Yu, Ming He, Jianping Fan:
Paragon: Parameter Generation for Controllable Multi-Task Recommendation. 370-380 - Xiangyi 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:
PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform. 381-390 - Yuyue Chen, Peng Yang, Zoe Lin Jiang, Wenhao Wu, Junbin Fang, Xuan Wang, Chuanyi Liu:
Privacy-Preserving Social Recommendation: Privacy Leakage and Countermeasure. 391-400 - Federico Tomasi, Francesco Fabbri, Justin Carter, Elias Kalomiris, Mounia Lalmas, Zhenwen Dai:
Prompt-to-Slate: Diffusion Models for Prompt-Conditioned Slate Generation. 401-410 - Hao Gu, Rui Zhong, Yu Xia, Wei Yang, Chi Lu, Peng Jiang, Kun Gai:
R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems. 411-421 - Md Sanzeed Anwar, Paramveer S. Dhillon, Grant Schoenebeck:
Recommendation and Temptation. 422-431 - Jiajie He, Yuechun Gu, Keke Chen:
RecPS: Privacy Risk Scoring for Recommender Systems. 432-440 - Barrie Kersbergen, Olivier Sprangers, Bojan Karlas, Maarten de Rijke, Sebastian Schelter:
Scalable Data Debugging for Neighborhood-based Recommendation with Data Shapley Values. 441-450 - Mingfan Pan, Qingyang Mao, Xu An, Jianhui Ma, Gang Zhou, Mingyue Cheng, Enhong Chen:
Tag-augmented Dual-target Cross-domain Recommendation. 451-460 - Changshuo Zhang, Xiao Zhang, Teng Shi, Jun Xu, Ji-Rong Wen:
Test-Time Alignment with State Space Model for Tracking User Interest Shifts in Sequential Recommendation. 461-471 - Jianyu Wen, Jingyun Wang, Cilin Yan, Jiayin Cai, Xiaolong Jiang, Ying Zhang:
USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model. 472-481 - Ramin Giahi, Kehui Yao, Sriram Kollipara, Kai Zhao, Vahid Mirjalili, Jianpeng Xu, Topojoy Biswas, Evren Körpeoglu, Kannan Achan:
VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP Embeddings. 482-491 - Giovanni De Toni, Erasmo Purificato, Emilia Gómez, Andrea Passerini, Bruno Lepri, Cristian Consonni:
You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control. 492-502
Short Papers
- Florian Atzenhofer-Baumgartner, Georg Vogeler, Dominik Kowald:
A Multistakeholder Approach to Value-Driven Co-Design of Recommender Systems Evaluation Metrics in Digital Archives. 503-508 - Viet-Anh Tran, Bruno Sguerra, Gabriel Meseguer-Brocal, Léa Briand, Manuel Moussallam:
"Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation. 509-514 - Amir Reza Mohammadi, Andreas Peintner, Michael Müller, Eva Zangerle:
Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender Systems. 515-520 - Zehui Wang, Wolfram Höpken, Dietmar Jannach:
Beyond Visit Trajectories: Enhancing POI Recommendation via LLM-Augmented Text and Image Representations. 521-526 - Bruno Sguerra, Elena V. Epure, Harin Lee, Manuel Moussallam:
Biases in LLM-Generated Musical Taste Profiles for Recommendation. 527-532 - Yu-Ting Cheng, Yu-Yen Ho, Jyun-Yu Jiang:
Collaborative Interest Modeling in Recommender Systems. 533-538 - Cedric Waterschoot, Nava Tintarev, Francesco Barile:
Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations. 539-544 - Kirill Khrylchenko, Vladimir Baikalov, Sergei S. Makeev, Artem Matveev, Sergei Liamaev:
Correcting the LogQ Correction: Revisiting Sampled Softmax for Large-Scale Retrieval. 545-550 - Olivier Jeunen:
Counterfactual Inference under Thompson Sampling. 551-557 - Runze Li, Lucien Heitz, Oana Inel, Abraham Bernstein:
D-RDW: Diversity-Driven Random Walks for News Recommender Systems. 558-563 - Yuki Ninomiya, Yutaro Sone, Kazuhisa Miwa, Yuichiro Sumi, Ryosuke Nakanishi, Eiji Mitsuda, Koji Sato, Tadashi Odashima:
Determinants of Users' Chance-Seeking Behavior in Search-Based Recommendation. 564-569 - Kaiyue Liu, Yang Liu, Alan Medlar, Dorota Glowacka:
Disentangling User and Item Sequence Patterns in Sequential Recommendation Data Sets. 570-574 - Matteo Attimonelli, Alessandro De Bellis, Claudio Pomo, Dietmar Jannach, Eugenio Di Sciascio, Tommaso Di Noia:
Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search. 575-580 - Kate Hill, Yiu-Kai Ng, Joey Sherrill:
Emotion Vector-Based Fine-Tuning of Large Language Models for Age-Aware Teenage Book Recommendations. 581-586 - Jiayang Niu, Qihan Zou, Jie Li, Ke Deng, Mark Sanderson, Yongli Ren:
Estimating Quantum Execution Requirements for Feature Selection in Recommender Systems Using Extreme Value Theory. 587-592 - Andrea Forster, Simone Kopeinik, Denis Helic, Stefan Thalmann, Dominik Kowald:
Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations. 593-598 - Maria Vlachou:
Failure Prediction in Conversational Recommendation Systems. 599-604 - Alec Nonnemaker, Ralvi Isufaj, Zoltán Szlávik, Cynthia C. S. Liem:
Feedback-Driven Gradual Discovery for Expanding Musical Preferences. 605-609 - Yamini Jha, Krishna Tewari, Sukomal Pal:
HiDePCC: A Novel Dual-Pronged Untargeted Attack on Federated Recommendation via Gradient Perturbation and Cluster Crafting. 610-614 - Alessandro B. Melchiorre, Elena V. Epure, Shahed Masoudian, Gustavo Escobedo, Anna Hausberger, Manuel Moussallam, Markus Schedl:
Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation. 615-620 - Yonatan Hadar, Yotam Eshel, Tal Franji, Bracha Shapira, Michelle Hwang, Guy Feigenblat:
Large Scale E-Commerce Model for Learning and Analyzing Long-Term User Preferences. 621-625 - Anton Pembek, Artem Fatkulin, Anton Klenitskiy, Alexey Vasilev:
Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization. 626-631 - David Penz, Gustavo Junior Escobedo Ticona, Markus Schedl:
Mitigating Latent User Biases in Pre-trained VAE Recommendation Models via On-demand Input Space Transformation. 632-636 - Wei-Wei Du, Takuma Udagawa, Kei Tateno:
Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation. 637-642 - Hanne Vandenbroucke, Ulysse Maes, Lien Michiels, Annelien Smets:
Not One News Recommender To Fit Them All: How Different Recommender Strategies Serve Various User Segments. 643-648 - Gregor Meehan, Johan Pauwels:
On Inherited Popularity Bias in Cold-Start Item Recommendation. 649-654 - Havva Alizadeh Noughabi, Behshid Behkamal, Fattane Zarrinkalam, Mohsen Kahani:
Personalized Persuasion-Aware Explanations in Recommender Systems. 655-659 - Kenji Shinoda, Takeyuki Sasai, Shintaro Fukushima:
Popularity‑Bias Vulnerability: Semi‑Supervised Label Inference Attack on Federated Recommender Systems. 660-665 - Woo-Seong Yun, Yeojun Choi, Yoon-Sik Cho:
Rethinking Overconfidence in VAEs: Can Label Smoothing Help? 666-670 - Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Yuanjie Zhu, Philip S. Yu:
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation. 671-676 - Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Falk Scholer, Christina Lioma:
Stairway to Fairness: Connecting Group and Individual Fairness. 677-683 - Gabrielle Alves, Dietmar Jannach, Luan Soares de Souza, Marcelo Garcia Manzato:
Towards Personality-Aware Explanations for Music Recommendations Using Generative AI. 684-689 - Chao-Chin Liu, Hao-Ren Yao, Der-Chen Chang, Ophir Frieder:
TreatRAG: A Framework for Personalized Treatment Recommendation. 690-695
Reproducibility Papers
- Huy-Son Nguyen, Yuanna Liu, Masoud Mansoury, Mohammad Aliannejadi, Alan Hanjalic, Maarten de Rijke:
A Reproducibility Study of Product-side Fairness in Bundle Recommendation. 696-705 - Guohang Zeng, Jie Lu, Guangquan Zhang:
Are We Really Making Recommendations Robust? Revisiting Model Evaluation for Denoising Recommendation. 706-715 - Pablo Sánchez, Alejandro Bellogín, Jose L. Jorro-Aragoneses:
Context Trails: A Dataset to Study Contextual and Route Recommendation. 716-725 - Alessandro Francesco Maria Martina, Alessandro Petruzzelli, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro:
DistillRecDial: A Knowledge-Distilled Dataset Capturing User Diversity in Conversational Recommendation. 726-735 - Pedro R. Pires, Gregório F. Azevedo, Pietro Lo Presti Campos, Rafael T. Sereicikas, Tiago A. Almeida:
Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation. 736-745 - Li Kang, Yuhan Zhao, Li Chen:
Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems. 746-754 - Maria Vlachou:
Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant Items. 755-763 - Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras, Giacomo Medda, Giovanni Murgia:
GreenFoodLens: Sustainability Labels for Food Recommendation. 764-773 - Maria Lucia Fioretti, Nicola Laterza, Alessia Preziosa, Daniele Malitesta, Claudio Pomo, Fedelucio Narducci, Tommaso Di Noia:
How Powerful are LLMs to Support Multimodal Recommendation? A Reproducibility Study of LLMRec. 774-782 - Robin Ungruh, Alejandro Bellogín, Dominik Kowald, Maria Soledad Pera:
Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study. 783-791 - Lucien Heitz, Runze Li, Oana Inel, Abraham Bernstein:
Informfully Recommenders - Reproducibility Framework for Diversity-aware Intra-session Recommendations. 792-801 - Jujia Zhao, Yumeng Wang, Zhaochun Ren, Suzan Verberne:
Model Meets Knowledge: Analyzing Knowledge Types for Conversational Recommender Systems. 802-811 - Yubo Wang, Min Tang, Nuo Shen, Shujie Cui, Weiqing Wang:
Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective. 812-821 - Dominykas Seputis, Yongkang Li, Karsten Langerak, Serghei Mihailov:
Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text". 822-831 - Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka:
Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation. 832-841 - Faisal Shehzad, Dietmar Jannach:
Revisiting the Performance of Graph Neural Networks for Session-based Recommendation. 842-846 - Giuseppe Spillo, Elio Musacchio, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro:
See the Movie, Hear the Song, Read the Book: Extending MovieLens-1M, Last.fm-2K, and DBbook with Multimodal Data. 847-856 - Ralvi Isufaj, Ruslan Tsygankov, Zoltán Szlávik:
The XITE Million Sessions Dataset. 857-864 - Alessandro Sbandi, Federico Siciliano, Fabrizio Silvestri:
TIM-Rec: Explicit Sparse Feedback on Multi-Item Upselling Recommendations in an Industrial Dataset of Telco Calls. 865-873 - Danil Gusak, Anna Volodkevich, Anton Klenitskiy, Alexey Vasilev, Evgeny Frolov:
Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders. 874-883 - Faisal Shehzad, Timo Breuer, Maria Maistro, Dietmar Jannach:
"We Share Our Code Online": Why This Is Not Enough to Ensure Reproducibility and Progress in Recommender Systems Research. 884-893 - Alexander Ploshkin, Vladislav Tytskiy, Alexey Pismenny, Vladimir Baikalov, Evgeny Taychinov, Artem Permiakov, Daniil Burlakov, Eugene Krofto:
Yambda-5B - A Large-Scale Multi-Modal Dataset for Ranking and Retrieval. 894-901
Industry track
- Yuta Hagio, Chigusa Yamamura, Hiromu Ogawa, Hisayuki Ohmata, Arisa Fujii:
A Media Content Recommendation Method for Playlist Curators using LLM-Based Query Expansion. 902-906 - Sami Abboud, Eleanor Hanna, Olivier Jeunen, Vineesha Raheja, Schaun Wheeler:
Agentic Personalisation of Cross-Channel Marketing Experiences. 907-910 - Mate Hartstein, Eva Giannatou, Martin Tegner:
An Analysis of Learned Product Embeddings in an E-Commerce Context. 911-914 - Marcel Hauck, Michael Huber, Juri Diels, David Wittenberg, Dietmar Jannach:
Balanced Public Service Media Recommendation Trade-offs with a Light Carbon Footprint. 915-918 - Changping Meng, Hongyi Ling, Jianling Wang, Yifan Liu, Shuzhou Zhang, Dapeng Hong, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Haokai Lu, Ningren Han:
Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation Updates. 919-922 - Mahanth 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:
Closing the Online-Offline Gap: A Scalable Framework for Composed Model Evaluation. 923-926 - Yunan Hu, Mark Thornburg, Mario García-Armas, Vito Ostuni, Anne Cocos, Kriti Kohli, Christoph Kofler, Rob Saltiel:
Cold Starting a New Content Type: A Case Study with Netflix Live. 927-930 - Akira Fukumoto, Aghiles Salah, Sarthak Shrivastava, Alexandru Tatar, Yannick Schwartz, Vincent Michel, Lee Xiong:
Contrastive Conditional Embeddings for Item-based Recommendation at E-commerce Scale. 931-934 - Jonathan Valverde, Tiansheng Yao, Xiang Li, Yuan Gao, Yin Zhang, Andrew Evdokimov, Adam Kraft, Samuel Ieong, Jerry Zhang, Ed H. Chi, Derek Zhiyuan Cheng, Ruoxi Wang:
Cross-Batch Aggregation for Streaming Learning from Label Proportions in Industrial-Scale Recommendation Systems. 935-939 - Jie 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:
Decoupled Entity Representation Learning for Pinterest Ads Ranking. 940-944 - Xiao Yang, Mehdi Ayed, Longyu Zhao, Fan Zhou, Yuchen Shen, Abe Engle, Jinfeng Zhuang, Ling Leng, Jiajing Xu, Charles Rosenberg, Prathibha Deshikachar:
Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest. 945-948 - Chelsea Weaver, Arvind Balasubramanian, Juan Borgnino, Ben London:
Efficient Off-Policy Evaluation of Content Blending in Station-Based Music Experiences. 949-953 - Carolina Zheng, Minhui Huang, Dmitrii Pedchenko, Kaushik Rangadurai, Siyu Wang, Fan Xia, Gaby Nahum, Jie Lei, Yang Yang, Tao Liu, Zutian Luo, Xiaohan Wei, Dinesh Ramasamy, Jiyan Yang, Yiping Han, Lin Yang, Hangjun Xu, Rong Jin, Shuang Yang:
Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID. 954-957 - Gwendolyn 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:
Enhancing Online Ranking Systems via Multi-Surface Co-Training for Content Understanding. 958-961 - Ghazal Fazelnia, Sanket Gupta, Claire Keum, Mark Koh, Timothy Christopher Heath, Guillermo Carrasco Hernández, Stephen Xie, Nandini Singh, Ian Anderson, Maya Hristakeva, Petter Pehrson Skidén, Mounia Lalmas:
Generalized User Representations for Large-Scale Recommendations and Downstream Tasks. 962-966 - Timo Wilm, Philipp Normann:
Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems. 967-970 - Mengxi Lv, Drew Hogg, Thomas Grubb, Shashank Bassi, Min Li, Cayman Simpson, Senthil Rajagopalan:
Improve the Personalization of Large-Scale Ranking Systems by Integrating User Survey Feedback. 971-974 - Yuki Yada, Sho Akiyama, Ryo Watanabe, Yuta Ueno, Yusuke Shido, Andre Rusli:
Improving Visual Recommendation on E-commerce Platforms Using Vision-Language Models. 975-978 - Xurong Liang, Vu Nguyen, Vuong Le, Paul Albert, Julien Monteil:
In-context Learning for Addressing User Cold-start in Sequential Movie Recommenders. 979-982 - Yunus Lutz, Timo Wilm, Philipp Duwe:
Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank. 983-986 - Dong Wang, Junyi Jiao, Arnab Bhadury, Yaping Zhang, Mingyan Gao, Onkar Dalal:
Item-centric Exploration for Cold Start Problem. 987-990 - George Barrowclough, Marian Andrecki, James Shinner, Daniele Donghi:
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing. 991-994 - Mattia Ottoborgo:
LADDER: LLM-Annotated Data for Dogfooded Evaluation of Rankings. 995-998 - Madhura Raju, Manisha Sharma, Hongyu Xiong, Bingfeng Deng, Meng Na:
Leveraging Explicit Negative Feedback in Large-Scale Recommendation Systems: A Case Study. 999-1001 - Boyuan 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:
LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations. 1002-1005 - Ivo Silva, Guilherme G. Bonaldo, Pedro F. Nogueira:
Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search. 1006-1009 - Sinan Zhu, Sanja Simonovikj, Darren Edmonds, Yang Sun:
Metadata Generation and Evaluation using LLMs - Case Study on Canonical Titles. 1010-1013 - Suman Malani, Youwei Zhang, Liang Liu:
Minimize Negative Experiences in Video Recommendation Systems with Multimodal Large Language Models. 1014-1016 - Aditee Kumthekar, Li Wei, Andrea Bettale, Mahesh Sathiamoorthy, Zrinka Puljiz, Aditya Mahajan:
Never Miss an Episode: How LLMs are Powering Serial Content Discovery on YouTube. 1017-1021 - Yuyan Wang, Jing Zhong, Yuxin Cui, Zhaohui Guo, Chuanqi Wei, Yanchen Wang, Zellux Wang:
Not All Impressions Are Created Equal: Psychology-Informed Retention Optimization for Short-Form Video Recommendation. 1022-1025 - Vivek Singh, Sarith Mohan, Chetan L. Srinidhi, Santosh Pai, Ullaskrishnan Poikavila, Codruta Ene, Ankur Kapoor, Neil Biehn, Dorin Comaniciu:
Operational Twin-Driven AI Recommender for Strategic Service Planning. 1026-1029 - Kevin Zielnicki, Ko-Jen Hsiao:
Orthogonal Low Rank Embedding Stabilization. 1030-1033 - Shaghayegh Agah, Shaun Schaeffer, Maria Peifer, Neeraj Sharma, Ankit Maheshwari, Sardar Hamidian:
Pareto-Optimal Solution: Optimizing Engagement and Revenue. 1034-1037 - Oded Zinman, Nazmul Chowdhury, Leandro Fiaschetti, Yuri M. Brovman, Guy Feigenblat, Yotam Eshel:
Personalized Interest Graphs for Theme-Driven User Behavior. 1038-1041 - Yuval Dishi, Ophir Friedler, Yonatan Karni, Natalia Silberstein, Yulia Stolin:
Practical Multi-Task Learning for Rare Conversions in Ad Tech. 1042-1045 - Amit Jaspal, Qian Dang, Ajantha Ramineni:
RADAR: Recall Augmentation through Deferred Asynchronous Retrieval. 1046-1049 - Renzhi Wu, Junjie Yang, Li Chen, Hong Li, Li Yu, Hong Yan:
RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation. 1050-1053 - Venkata Harshit Koneru, Xenija Neufeld, Sebastian Loth, Andreas Grün:
SASRec in Action: Real-World Adaptations for ZDF Streaming Service. 1054-1057 - Yue Dong, Han Li, Shen Li, Nikhil Patel, Xing Liu, Xiaodong Wang, Chuanhao Zhuge:
Scaling Generative Recommendations with Context Parallelism on Hierarchical Sequential Transducers. 1058-1061 - Haiyun Jin, Bobby Patel:
Scaling Image Variant Optimization Through Customer Bucketing and Response Caching: A Large-Scale Implementation at Amazon Prime Video. 1062-1065 - Yuchin Juan, Jianqiang Shen, Shaobo Zhang, Qianqi Shen, Caleb Johnson, Luke Simon, Liangjie Hong, Wenjing Zhang:
Scaling Retrieval for Web-Scale Recommenders: Lessons from Inverted Indexes to Embedding Search. 1066-1069 - M. Jeffrey Mei, Florian Henkel, Samuel E. Sandberg, Oliver Bembom, Andreas F. Ehmann:
Semantic IDs for Music Recommendation. 1070-1073 - Sofia Maria Nikolakaki, Siyong Ma, Srivas Chennu, Humeyra Topcu Altintas:
SEMORec: A Scalarized Efficient Multi-Objective Recommendation Framework. 1074-1077 - Adeep Hande, Kishorekumar Sundararajan, Yidnekachew Endale, Sardar Hamidian:
Simulating Discoverability for Upcoming Content in TV Entertainment Platforms. 1078-1081 - Amit Jaspal, Kapil Dalwani, Ajantha Ramineni:
SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations. 1082-1085 - Yizhou Sang, Congcong Liu, Yuying Chen, Zhiwei Fang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao:
Stream Normalization for CTR Prediction. 1086-1090 - Yang Gu, Caroline Zhou, Qiao Zhang, Scott Wang, Yongzhe Wang, Li Zhang, Nikos Parotsidis, CJ Carey, Ashkan Fard, Mingyan Gao, Yaping Zhang, Sourabh Bansod:
Streaming Trends: A Low-Latency Platform for Dynamic Video Grouping and Trending Corpora Building. 1091-1094 - Aleksandra Maria Osowska-Kurczab, Klaudia Nazarko, Mateusz Marzec, Lidia Wojciechowska, Eliska Kremenová:
Suggest, Complement, Inspire: Story of Two-Tower Recommendations at Allegro.com. 1095-1098 - Petr Kasalický, Martin Spisák, Vojtech Vancura, Daniel Bohunek, Rodrigo Alves, Pavel Kordík:
The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems. 1099-1103 - Chenghui Yu, Haoze Wu, Jian Ding, Bingfeng Deng, Hongyu Xiong:
Unified Survey Modeling to Limit Negative User Experiences in Recommendation Systems. 1104-1107 - Jiaqi Zheng, Cheng Guo, Yi Cao, Chaoqun Hou, Tong Liu, Bo Zheng:
USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations. 1108-1111 - Yue Meng, Cheng Guo, Xiaohui Hu, Honghu Deng, Yi Cao, Tong Liu, Bo Zheng:
User Long-Term Multi-Interest Retrieval Model for Recommendation. 1112-1116 - Enrico 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:
You Say Search, I Say Recs: A Scalable Agentic Approach to Query Understanding and Exploratory Search at Spotify. 1117-1121 - Srivaths Ranganathan, Chieh Lo, Bernardo Cunha, Nikhil Khani, Li Wei, Aniruddh Nath, Shawn Andrews, Gergo Varady, Yanwei Song, Jochen Klingenhoefer, Tim Steele:
Zero-shot Cross-domain Knowledge Distillation: A Case study on YouTube Music. 1122-1125
Late-breaking Results
- Minje Kim, Wooseung Kang, Gun-Woo Kim, Chie Hoon Song, Suwon Lee, Sang-Min Choi:
A Dual-Key Attention Framework for Sequential Recommendation with Side Information. 1126-1131 - Oren Sar Shalom, Neil Daftary:
Addressing Multiple Hypothesis Bias in CTR Prediction for Ad Selection. 1132-1136 - Chiara Mallamaci, Aleksandr V. Petrov, Alberto Carlo Maria Mancino, Vito Walter Anelli, Tommaso Di Noia, Craig Macdonald:
Balancing Accuracy and Novelty with Sub-Item Popularity. 1137-1141 - Veronika Ivanova, Evgeny Frolov, Alexey Vasilev:
Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns. 1142-1147 - Georgios Koutroumpas, Matteo Mazzini, Sebastian Idesis, Mireia Masias Bruns, Joemon M. Jose, Sergi Abadal, Ioannis Arapakis:
Beyond Clicks: Eye-Tracking Insights into User Responses to Different Recommendation Types. 1148-1152 - Gustavo Escobedo, David Penz, Markus Schedl:
Debiasing Implicit Feedback Recommenders via Sliced Wasserstein Distance-based Regularization. 1153-1158 - Marco De Nadai, Andreas Damianou, Mounia Lalmas:
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations. 1159-1163 - Huy Hoang Le, Yang Liu, Alan Medlar, Dorota Glowacka:
Don't Get Ahead of Yourself: A Critical Study on Data Leakage in Offline Evaluation of Sequential Recommenders. 1164-1168 - Minje Kim, Wooseung Kang, Gun-Woo Kim, Chie Hoon Song, Suwon Lee, Sang-Min Choi:
End-to-End Time Interval-wise Segmentation for Sequential Recommendation. 1169-1174 - Daria Tikhonovich, Nikita Zelinskiy, Aleksandr V. Petrov, Mayya Spirina, Andrei Semenov, Andrey V. Savchenko, Sergei Kuliev:
eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashion. 1175-1180 - Francesco Fabbri, Gustavo Penha, Edoardo D'Amico, Alice Wang, Marco De Nadai, Jackie Doremus, Paul Gigioli, Andreas Damianou, Oskar Stål, Mounia Lalmas:
Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge. 1181-1186 - Oleg Lesota, Adrian Bajko, Max Walder, Matthias Wenzel, Antonela Tommasel, Markus Schedl:
Fine-tuning for Inference-efficient Calibrated Recommendations. 1187-1192 - Robin Ungruh, Alejandro Bellogín, Maria Soledad Pera:
From Previous Plays to Long-Term Tastes: Exploring the Long-term Reliability of Recommender Systems Simulations for Children. 1193-1198 - Daniele Malitesta, Giacomo Medda, Erasmo Purificato, Mirko Marras, Fragkiskos D. Malliaros, Ludovico Boratto:
How Fair is Your Diffusion Recommender Model? 1199-1205 - Josef Schodl, Oleg Lesota, Antonela Tommasel, Markus Schedl:
Investigating Carbon Footprint of Recommender Systems Beyond Training Time. 1206-1211 - Zaira Zainulabidova, Julia Borisova, Alexander Hvatov:
Learning geometry-aware recommender systems with manifold regularization. 1212-1216 - Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov:
Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations. 1217-1221 - Gaetano Dibenedetto, Pasquale Lops, Marco Polignano, Helma Torkamaan:
Lift It Up Right: A Recommender System for Safer Lifting Postures. 1222-1227 - Olivier Jeunen:
Meta Off-Policy Estimation. 1228-1233 - Arjan Hasami, Masoud Mansoury:
Mitigating Popularity Bias in Counterfactual Explanations using Large Language Models. 1234-1239 - Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Ryotaro Shimizu, Julian J. McAuley, Pierre-Alexandre Mattei, Jes Frellsen:
Normative Alignment of Recommender Systems via Internal Label Shift. 1240-1245 - Parviz Ahmadov, Masoud Mansoury:
Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems. 1246-1250 - Eran Fainman, Adir Solomon, Osnat Mokryn:
PAIRSAT: Integrating Preference-Based Signals for User Satisfaction Estimation in Dialogue Systems. 1251-1255 - Marta Moscati, Shah Nawaz, Markus Schedl:
Parameter-Efficient Single Collaborative Branch for Recommendation. 1256-1260 - Jennifer Poernomo, Nicole Gabrielle Lee Tan, Rodrigo Alves, Antoine Ledent:
Probabilistic Modeling, Learnability and Uncertainty Estimation for Interaction Prediction in Movie Rating Datasets. 1261-1266 - Danil Gusak, Nikita Sukhorukov, Evgeny Frolov:
Recommendation Is a Dish Better Served Warm. 1267-1272 - Tereza Zmeskalová, Antoine Ledent, Martin Spisák, Pavel Kordík, Rodrigo Alves:
Recurrent Autoregressive Linear Model for Next-Basket Recommendation. 1273-1278 - Arsen Matej Golubovikj, Marko Tkalcic:
Rethinking Subjective Features in Recommender Systems: Personal Views Over Aggregated Values. 1279-1283 - Zhongtian Sun, Anoushka Harit:
RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs. 1284-1289 - Vit Kostejn, Ladislav Peska, Martin Spisák:
SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations. 1290-1295 - Gustavo Penha, Edoardo D'Amico, Marco De Nadai, Enrico Palumbo, Alexandre Tamborrino, Ali Vardasbi, Max Lefarov, Shawn Lin, Timothy Christopher Heath, Francesco Fabbri, Hugues Bouchard:
Semantic IDs for Joint Generative Search and Recommendation. 1296-1301 - Aayush Singha Roy, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley:
SlateLLM: Distilling LLM Semantics into Session-Aware Slate Recommendation without Inference Overhead. 1302-1306 - Olivier Jeunen:
t-Testing the Waters: Empirically Validating Assumptions for Reliable A/B-Testing. 1307-1310 - Hannah Berling, Robin Svahn, Alan Said:
The Hidden Cost of Defaults in Recommender System Evaluation. 1311-1316 - Masahiro Sato:
Unobserved Negative Items in Recommender Systems: Challenges and Solutions for Evaluation and Learning. 1317-1321
Demonstrations
- Tobias Vente, Michael Heep, Abdullah Abbas, Theodor Sperle, Joeran Beel, Bart Goethals:
APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection. 1322-1324 - Bereket Abera Yilma, Saravanakumar Duraisamy, Stefan Penchev, Tudor Pristav, Luis A. Leiva:
ArtAICare: An End-to-End Platform for Personalized Art Therapy. 1325-1327 - Rully Agus Hendrawan, Peter Brusilovsky, Luis A. Leiva, Bereket Abera Yilma:
ArtEx: A User-Controllable Web Interface for Visual Art Recommendations. 1328-1330 - Ibrahim Al Hazwani, Daniel Lutziger, Carlos Kirchdorfer, Luca Huber, Oliver Robin Aschwanden, Jürgen Bernard, Ludovico Boratto:
Blooming Beats: An Interactive Music Recommender System Grounded in TRACE Principles and Data Humanism. 1331-1333 - Akash Khetan, Narasimha Medeme, Deepak Yadav, Anmol Porwal:
Flights Pricelock Fee Recommendation on Online Travel Agent Platform. 1334-1336 - Eléa Vellard, Enzo Charolois-Pasqua, Youssra Rebboud, Pasquale Lisena, Raphaël Troncy:
Interactive Playlist Generation from Titles. 1337-1339 - Tommaso Carraro, Brijraj Singh, Niranjan Pedanekar:
Large Language Model-based Recommendation System Agents. 1340-1342 - Ibrahim Al Hazwani, Oliver Robin Aschwanden, Oana Inel, Jürgen Bernard, Ludovico Boratto:
PRISM: From Individual Preferences to Group Consensus through Conversational AI-Mediated and Visual Explanations. 1343-1345 - Jackson Dam, Zixuan Yi, Iadh Ounis:
RecViz: Intuitive Graph-based Visual Analytics for Dataset Exploration and Recommender System Evaluation. 1346-1348 - Patrícia Alves, Joana Neto, Jorge Lima, José Silva, Luís Conceição, Goreti Marreiros:
Travel Together, Play Together: Gamifying a Group Recommender System for Tourism. 1349-1352 - Bulat Khaertdinov, Mirela Popa, Nava Tintarev:
VisualReF: Interactive Image Search Prototype with Visual Relevance Feedback. 1353-1356
Workshops and Challenge
- Peter Brusilovsky, Alexander Felfernig, Pasquale Lops, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen:
12th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'25). 1357-1359 - Eva Zangerle, Alan Said, Christine Bauer:
Beyond Algorithms: Reclaiming the Interdisciplinary Roots of Recommender Systems (BEYOND 2025). 1360-1361 - Harrie Oosterhuis, Olivier Jeunen, Yuta Saito, Yixin Wang, Flavian Vasile, Thorsten Joachims:
CONSEQUENCES 2025 - The 4th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. 1362-1365 - Irene Li, Ruihai Dong, Guillaume Salha-Galvan, Aonghus Lawlor, Dairui Liu, Lei Li:
EARL: The 2nd Workshop on Evaluating and Applying Recommender Systems with Large Language Models. 1366-1370 - Michael D. Ekstrand, Toshihiro Kamishima, Amifa Raj, Karlijn Dinnissen:
FAccTRec 2025: The 8th Workshop on Responsible Recommendation. 1371-1372 - Toine Bogers, Mesut Kaya, Jens-Joris Decorte, Chris Johnson, Guillaume Bied:
Fifth Workshop on Recommender Systems for Human Resources (RecSys in HR 2025). 1373-1377 - Claudio Pomo, Dietmar Jannach, Yubin Kim, Daniele Malitesta, Alberto Carlo Maria Mancino, Julian J. McAuley, Alessandro B. Melchiorre, Shah Nawaz:
First International Workshop on Data Quality-Aware Multimodal Recommendation (DaQuaMRec). 1378-1382 - Andres Ferraro, Lorenzo Porcaro, Christine Bauer:
MuRS: 3rd Music Recommender Systems Workshop. 1383-1385 - Lien Michiels, Sanne Vrijenhoek, Alain D. Starke, Johannes Kruse, Savvina Daniil:
NORMalize 2025: The Third Workshop on Normative Design and Evaluation of Recommender Systems. 1386-1388 - Jacek Dabrowski, Maria Janicka, Lukasz Sienkiewicz, Gergely Stomfai, Dietmar Jannach, Francesco Barile, Marco Polignano, Claudio Pomo, Abhishek Srivastava:
RecSys Challenge 2025: Universal Behavioral Profiles for Recommender Systems. 1389-1393 - Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, Francesca Maridina Malloci, Noemi Mauro, Francesco Ricci:
Second International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2025). 1394-1398 - Andreea Iana, Célina Treuillier, Vandana Yadav, Benjamin Kille, Andreas Lommatzsch, Özlem Özgöbek:
The 13th International Workshop on News Recommendation and Analytics (INRA 2025). 1399-1403 - Mansi Ranjit Mane, Djordje Gligorijevic, Dingxian Wang, Topojoy Biswas, Evren Körpeoglu, Marios Savvides, Yongfeng Zhang, Julian J. McAuley:
The Second Workshop on Generative AI for E-commerce. 1404-1408 - Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems. 1409-1411 - Julia Neidhardt, Tsvi Kuflik, Amit Livne, Markus Zanker, Wolfgang Wörndl:
Workshop on Recommenders in Tourism (RecTour) 2025. 1412-1413
Tutorials
- Maurizio Ferrari Dacrema, Paolo Cremonesi:
A Hands-on Dive Into Quantum Computing for Recommender Systems. 1414-1416 - Chengkai Huang, Junda Wu, Tong Yu, Julian J. McAuley, Lina Yao:
A Tutorial on Agentic LLM for Recommender Systems. 1417-1419 - Thomas Elmar Kolb, Ahmadou Wagne, Ashmi Banerjee, Fatemeh Nazary, Julia Neidhardt, Yashar Deldjoo, Tommaso Di Noia:
A Tutorial on Recent Advances in Generative Conversational Recommender Systems. 1420-1422 - Omprakash Sonie:
concept2code: Sequential Recommendation with Large Language Models. 1423-1424 - João Vinagre, Lorenzo Porcaro, Silvia Merisio, Erasmo Purificato, Emilia Gómez:
Data Access for Recommender Systems Research: leveraging the EU's Digital Services Act. 1425-1426 - Reza Yousefi Maragheh, Yashar Deldjoo, Chi Wang, Jason Cho, Derek Cheng:
Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications - An Industrial Tutorial. 1427-1429 - Kim Falk:
Multi-Armed Bandits in the Wild. 1430-1431 - Alberto Carlo Maria Mancino, Matteo Attimonelli, Angela Di Fazio, Daniele Malitesta, Tommaso Di Noia:
Standard Practices for Data Processing and Multimodal Feature Extraction in Recommendation with DataRec and Ducho (D&D4Rec). 1432-1434
Doctoral Symposium
- Cornelia M. Kloppers:
Adding Value to Low-Resource Industrial Recommender Systems. 1435-1438 - Amanda Aird:
Addressing Multi-stakeholder Fairness Concerns in Recommender Systems Through Social Choice. 1439-1444 - Michael Müller:
Advancing User-Centric Evaluation and Design of Conversational Recommender Systems. 1445-1450 - Robin Ungruh:
Are Recommender Systems Serving Children? Toward Child-Aware Design and Evaluation. 1451-1457 - Michael Benigni:
Bayesian Perspectives on Offline Evaluation for Recommender Systems. 1458-1462 - Elaheh Jafari:
Beyond Persuasion: Adaptive Warnings and Balanced Explanations for Informed Decision-Making in Recommender Systems. 1463-1468 - Elena-Ruxandra Lutan:
Challenges in Perfume Recommender Systems: Navigating Subjectivity, Context and Sensory Data. 1469-1472 - Dina Zilbershtein:
Fair and Transparent Recommender Systems for Advertisements. 1473-1478 - Jan Kislinger:
Full-Page Recommender: A Modular Framework for Multi-Carousel Recommendations. 1479-1484 - Fabio Ferrero:
Narrative-Driven Itinerary Recommendation: LLM Integration for Immersive Urban Walking. 1485-1491 - Gabriel Alfonso Patron:
Personalized Image Generation for Recommendations Beyond Catalogs. 1492-1498 - Florian Atzenhofer-Baumgartner:
Recommender Systems for Digital Humanities and Archives: Multistakeholder Evaluation, Scholarly Information Needs, and Multimodal Similarity. 1499-1505
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