Table of Contents

• The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender SystemsGuy Aridor, Duarte Gonçalves, Ruoyan Kong, Daniel Kluver, Joseph A. Konstan. 1 [doi]
• SeCor: Aligning Semantic and Collaborative Representations by Large Language Models for Next-Point-of-Interest RecommendationsShirui Wang, Bohan Xie, Ling Ding 0003, Xiaoying Gao, Jianting Chen, Yang Xiang. 1-11 [doi]
• Towards Open-World Recommendation with Knowledge Augmentation from Large Language ModelsYunjia Xi, Weiwen Liu, Jianghao Lin, Xiaoling Cai, Hong Zhu 0003, Jieming Zhu, Bo Chen 0023, Ruiming Tang, Weinan Zhang 0001, Yong Yu 0001. 12-22 [doi]
• LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic UnderstandingZhizhong Wan, Bin Yin, Junjie Xie, Fei Jiang, Xiang Li 0067, Wei Lin 0022. 23-32 [doi]
• Large Language Models as Evaluators for Recommendation ExplanationsXiaoyu Zhang, Yishan Li, Jiayin Wang, Bowen Sun, Weizhi Ma, Peijie Sun, Min Zhang 0006. 33-42 [doi]
• Unleashing the Retrieval Potential of Large Language Models in Conversational Recommender SystemsTing Yang, Li Chen. 43-52 [doi]
• The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential RecommendationZekai Qu, Ruobing Xie, Chaojun Xiao, Zhanhui Kang, Xingwu Sun. 53-62 [doi]
• ReLand: Integrating Large Language Models' Insights into Industrial Recommenders via a Controllable Reasoning PoolChangxin Tian, Binbin Hu, Chunjing Gan, Haoyu Chen, Zhuo Zhang, Li Yu, Ziqi Liu, Zhiqiang Zhang 0012, Jun Zhou 0011, Jiawei Chen. 63-73 [doi]
• Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference ElicitationDavid Eric Austin, Anton Korikov, Armin Toroghi, Scott Sanner. 74-83 [doi]
• Towards Empathetic Conversational Recommender SystemsXiaoyu Zhang, Ruobing Xie, Yougang Lyu, Xin Xin 0003, Pengjie Ren, Mingfei Liang, Bo Zhang 0056, Zhanhui Kang, Maarten de Rijke, Zhaochun Ren. 84-93 [doi]
• FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionHangyu Wang, Jianghao Lin, Xiangyang Li, Bo Chen 0023, Chenxu Zhu, Ruiming Tang, Weinan Zhang 0001, Yong Yu 0001. 94-104 [doi]
• A Comparative Analysis of Text-Based Explainable Recommender SystemsAlejandro Ariza-Casabona, Ludovico Boratto, Maria Salamó. 105-115 [doi]
• Reproducibility of LLM-based Recommender Systems: the Case Study of P5 ParadigmPasquale Lops, Antonio Silletti, Marco Polignano, Cataldo Musto, Giovanni Semeraro. 116-125 [doi]
• FairCRS: Towards User-oriented Fairness in Conversational Recommendation SystemsQin Liu, Xuan Feng, Tianlong Gu, Xiaoli Liu. 126-136 [doi]
• AMBAR: A dataset for Assessing Multiple Beyond-Accuracy RecommendersElizabeth Gómez, David Contreras, Ludovico Boratto, Maria Salamó. 137-147 [doi]
• Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming DataKristina Matrosova, Lilian Marey, Guillaume Salha-Galvan, Thomas Louail, Olivier Bodini, Manuel Moussallam. 148-157 [doi]
• Fair Augmentation for Graph Collaborative FilteringLudovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda. 158-168 [doi]
• Putting Popularity Bias Mitigation to the Test: A User-Centric Evaluation in Music RecommendersRobin Ungruh, Karlijn Dinnissen, Anja Volk, Maria Soledad Pera, Hanna Hauptmann. 169-178 [doi]
• Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval BiasLulu Dong, Guoxiu He, Aixin Sun. 179-188 [doi]
• Biased User History Synthesis for Personalized Long-Tail Item RecommendationKeshav Balasubramanian, Abdulla Alshabanah, Elan Markowitz, Greg Ver Steeg, Murali Annavaram. 189-199 [doi]
• The Fault in Our Recommendations: On the Perils of Optimizing the MeasurableOmar Besbes, Yash Kanoria, Akshit Kumar. 200-208 [doi]
• Fair Reciprocal Recommendation in Matching MarketsYoji Tomita, Tomohiko Yokoyama. 209-218 [doi]
• The Role of Unknown Interactions in Implicit Matrix Factorization - A Probabilistic ViewJoey De Pauw, Bart Goethals. 219-227 [doi]
• Adaptive Fusion of Multi-View for Graph Contrastive RecommendationMengduo Yang, Yi Yuan, Jie Zhou, Meng Xi, Xiaohua Pan, Ying Li, Yangyang Wu, Jinshan Zhang 0001, Jianwei Yin. 228-237 [doi]
• Low Rank Field-Weighted Factorization Machines for Low Latency Item RecommendationAlex Shtoff, Michael Viderman, Naama Haramaty-Krasne, Oren Somekh, Ariel Raviv, Tularam Ban. 238-246 [doi]
• Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled DataYuhan Zhao, Rui Chen 0012, Qilong Han, Hongtao Song, Li Chen 0009. 247-256 [doi]
• One-class Matrix Factorization: Point-Wise Regression-Based or Pair-Wise Ranking-Based?Sheng-Wei Chen 0003, Chih-Jen Lin. 257-266 [doi]
• Revisiting BPR: A Replicability Study of a Common Recommender System BaselineAleksandr Milogradskii, Oleg Lashinin, Alexander P, Marina Ananyeva, Sergey Kolesnikov. 267-277 [doi]
• Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial TrainingJingyu Chen, Lilin Zhang, Ning Yang. 278-286 [doi]
• MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR PredictionZhiming Yang, Haining Gao, Dehong Gao, Luwei Yang, Libin Yang, Xiaoyan Cai, Wei Ning, Guannan Zhang. 287-297 [doi]
• Instructing and Prompting Large Language Models for Explainable Cross-domain RecommendationsAlessandro Petruzzelli, Cataldo Musto, Lucrezia Laraspata, Ivan Rinaldi, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro. 298-308 [doi]
• Cross-Domain Latent Factors Sharing via Implicit Matrix FactorizationAbdulaziz Samra, Evgeny Frolov, Alexey Vasilev, Alexander Grigorevskiy, Anton Vakhrushev. 309-317 [doi]
• Discerning Canonical User Representation for Cross-Domain RecommendationSiqian Zhao, Sherry Sahebi. 318-328 [doi]
• Touch the Core: Exploring Task Dependence Among Hybrid Targets for RecommendationXing Tang 0007, Yang Qiao, Fuyuan Lyu, Dugang Liu, Xiuqiang He 0001. 329-339 [doi]
• Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?Gustavo Penha, Ali Vardasbi, Enrico Palumbo, Marco De Nadai, Hugues Bouchard. 340-349 [doi]
• Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate PredictionJiahui Huang, Lan Zhang 0002, Junhao Wang, Shanyang Jiang, Dongbo Huang, Cheng Ding, Lan Xu. 350-359 [doi]
• Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure SpaceYu Liu, Qinglin Jia, Shuting Shi, Chuhan Wu, Zhaocheng Du, Zheng Xie, Ruiming Tang, Muyu Zhang, Ming Li. 360-369 [doi]
• Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR PredictionWenhao Li, Jie Zhou, Chuan Luo, Chao Tang, Kun Zhang, Shixiong Zhao. 370-379 [doi]
• A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality ScenariosChristian Ganhör, Marta Moscati, Anna Hausberger, Shah Nawaz, Markus Schedl. 380-390 [doi]
• A Multi-modal Modeling Framework for Cold-start Short-video RecommendationGaode Chen, Ruina Sun, Yuezihan Jiang, Jiangxia Cao, Qi Zhang, Jingjian Lin, Han Li, Kun Gai, Xinghua Zhang 0001. 391-400 [doi]
• MARec: Metadata Alignment for cold-start RecommendationJulien Monteil, Volodymyr Vaskovych, Wentao Lu, Anirban Majumder, Anton van den Hengel. 401-410 [doi]
• Prompt Tuning for Item Cold-start RecommendationYuezihan Jiang, Gaode Chen, Wenhan Zhang 0004, Jingchi Wang, Yinjie Jiang, Qi Zhang, Jingjian Lin, Peng Jiang, Kaigui Bian. 411-421 [doi]
• CALRec: Contrastive Alignment of Generative LLMs for Sequential RecommendationYaoyiran Li, Xiang Zhai, Moustafa Alzantot, Keyi Yu, Ivan Vulic, Anna Korhonen, Mohamed Hammad. 422-432 [doi]
• A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity DynamicsJunting Wang, Praneet Rathi, Hari Sundaram. 433-443 [doi]
• Scaling Law of Large Sequential Recommendation ModelsGaowei Zhang, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ji-Rong Wen. 444-453 [doi]
• ReChorus2.0: A Modular and Task-Flexible Recommendation LibraryJiayu Li, Hanyu Li, Zhiyu He, Weizhi Ma, Peijie Sun, Min Zhang 0006, Shaoping Ma. 454-464 [doi]
• Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential RecommendationWeixin Li, Xiaolin Lin, Weike Pan, Zhong Ming 0001. 465-474 [doi]
• Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item CatalogsGleb Mezentsev, Danil Gusak, Ivan V. Oseledets, Evgeny Frolov. 475-485 [doi]
• Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session RecommendationViet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra, Romain Hennequin. 486-496 [doi]
• Repeated Padding for Sequential RecommendationYizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang 0001, Jianzhe Zhao. 497-506 [doi]
• Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language ModelsYu Cui, Feng Liu, Pengbo Wang, Bohao Wang, Heng Tang, Yi Wan, Jun Wang, Jiawei Chen. 507-517 [doi]
• A Unified Graph Transformer for Overcoming Isolations in Multi-modal RecommendationZixuan Yi, Iadh Ounis. 518-527 [doi]
• Information-Controllable Graph Contrastive Learning for RecommendationZirui Guo, Yanhua Yu, Yuling Wang, Kangkang Lu, Zixuan Yang, Liang Pang, Tat-Seng Chua. 528-537 [doi]
• MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for RecommendationsYuezihan Jiang, Changyu Li, Gaode Chen, Peiyi Li 0008, Qi Zhang, Jingjian Lin, Peng Jiang 0002, Fei Sun 0001, Wentao Zhang. 538-548 [doi]
• A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item GraphDaniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio. 549-559 [doi]
• End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift ModelingZexu Sun, Hao Yang, Dugang Liu, Yunpeng Weng, Xing Tang 0007, Xiuqiang He 0001. 560-569 [doi]
• Reproducibility and Analysis of Scientific Dataset Recommendation MethodsOrnella Irrera, Matteo Lissandrini, Daniele Dell'Aglio, Gianmaria Silvello. 570-579 [doi]
• From Clicks to Carbon: The Environmental Toll of Recommender SystemsTobias Vente, Lukas Wegmeth, Alan Said, Joeran Beel. 580-590 [doi]
• DNS-Rec: Data-aware Neural Architecture Search for Recommender SystemsSheng Zhang, Maolin Wang 0001, Xiangyu Zhao 0001, Ruocheng Guo, Yao Zhao, Chenyi Zhuang, Jinjie Gu, Zijian Zhang 0009, Hongzhi Yin. 591-600 [doi]
• ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive LearningXiao Yu, Jinzhong Zhang, Zhou Yu. 601-611 [doi]
• Unified Denoising Training for RecommendationHaoyan Chua, Yingpeng Du, Zhu Sun 0001, Ziyan Wang, Jie Zhang 0002, Yew-Soon Ong. 612-621 [doi]
• Embedding Optimization for Training Large-scale Deep Learning Recommendation Systems with EMBarkShijie Liu, Nan Zheng, Hui Kang, Xavier Simmons, Junjie Zhang, Matthias Langer, Wenjing Zhu, Minseok Lee, Zehuan Wang. 622-632 [doi]
• AIE: Auction Information Enhanced Framework for CTR Prediction in Online AdvertisingYang Yang, Bo Chen 0023, Chenxu Zhu, Menghui Zhu, Xinyi Dai, Huifeng Guo, Muyu Zhang, Zhenhua Dong, Ruiming Tang. 633-642 [doi]
• Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food DeliveryJiayu Li, Aixin Sun, Weizhi Ma, Peijie Sun, Min Zhang 0006. 643-653 [doi]
• Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life DataMahta Bakhshizadeh, Heiko Maus, Andreas Dengel 0001. 654-659 [doi]
• Informfully - Research Platform for Reproducible User StudiesLucien Heitz, Julian Andrea Croci, Madhav Sachdeva, Abraham Bernstein. 660-669 [doi]
• RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender SystemsShuo Su, Xiaoshuang Chen, Yao Wang, Yulin Wu, Ziqiang Zhang, Kaiqiao Zhan, Ben Wang, Kun Gai. 670-679 [doi]
• Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender SystemKaike Zhang, Qi Cao, Yunfan Wu, Fei Sun 0001, Huawei Shen, Xueqi Cheng. 680-689 [doi]
• FedLoCA: Low-Rank Coordinated Adaptation with Knowledge Decoupling for Federated RecommendationsYuchen Ding, Siqing Zhang, Boyu Fan, Wei Sun, Yong Liao, Peng Yuan Zhou. 690-700 [doi]
• Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender SystemsYunfan Wu, Qi Cao, Shuchang Tao, Kaike Zhang, Fei Sun 0001, Huawei Shen. 701-711 [doi]
• Multi-Objective Recommendation via Multivariate Policy LearningOlivier Jeunen, Jatin Mandav, Ivan Potapov, Nakul Agarwal, Sourabh Vaid, Wenzhe Shi, Aleksei Ustimenko. 712-721 [doi]
• Optimal Baseline Corrections for Off-Policy Contextual BanditsShashank Gupta 0001, Olivier Jeunen, Harrie Oosterhuis, Maarten de Rijke. 722-732 [doi]
• Effective Off-Policy Evaluation and Learning in Contextual Combinatorial BanditsTatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, Yuta Saito. 733-741 [doi]
• "More to Read" at the Los Angeles Times: Solving a Cold Start Problem with LLMs to Improve Story DiscoveryFranklin Horn, Aurelia Alston, Won J. You. 742-744 [doi]
• A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerceGuangtao Nie, Rong Zhi, Xiaofan Yan, Yufan Du, Xiangyang Zhang, Jianwei Chen, Mi Zhou, Hongshen Chen, Tianhao Li, Ziguang Cheng, Sulong Xu, Jinghe Hu. 745-747 [doi]
• AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code RecommendationsJan Hartman, Hitesh Sagtani, Julie Tibshirani, Rishabh Mehrotra. 748-750 [doi]
• Analyzing User Preferences and Quality Improvement on Bing's WebPage Recommendation Experience with Large Language ModelsJaidev Shah, Gang Luo, Jialin Liu 0007, Amey Barapatre, Fan Wu, Chuck Wang, Hongzhi Li. 751-754 [doi]
• Bootstrapping Conditional Retrieval for User-to-Item RecommendationsHongtao Lin, Haoyu Chen, Jaewon Yang, Jiajing Xu. 755-757 [doi]
• Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking SystemsNikhil Khani, Li Wei, Aniruddh Nath, Shawn Andrews, Shuo Yang, Yang Liu, Pendo Abbo, Maciej Kula, Jarrod Kahn, Zhe Zhao 0001, Lichan Hong, Ed H. Chi. 758-761 [doi]
• Co-optimize Content Generation and Consumption in a Large Scale Video Recommendation SystemZhen Zhang, Qingyun Liu, Yuening Li, Sourabh Bansod, Mingyan Gao, Yaping Zhang, Zhe Zhao 0001, Lichan Hong, Ed H. Chi, Shuchao Bi, Liang Liu. 762-764 [doi]
• Country-diverted experiments for mitigation of network effectsLina Lin, Changping Meng, Jennifer Brennan, Jean Pouget-Abadie, Ningren Han, Shuchao Bi, Yajun Peng. 765-767 [doi]
• Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerceÁdám Tibor Czapp, Mátyás Jani, Bálint Domián, Balázs Hidasi. 768-770 [doi]
• Embedding based retrieval for long tail search queries in ecommerceAkshay Kekuda, Yuyang Zhang, Arun Udayashankar. 771-774 [doi]
• Encouraging Exploration in Spotify Search through Query RecommendationsHenrik Lindstrom, Humberto Jesús Corona Pampín, Enrico Palumbo, Alva Liu. 775-777 [doi]
• Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash AttentionRengan Xu, Junjie Yang, Yifan Xu, Hong Li, Xing Liu, Devashish Shankar, Haoci Zhang, Meng Liu, Boyang Li, Yuxi Hu, Mingwei Tang, Zehua Zhang, Tunhou Zhang, Dai Li, Sijia Chen, Gian-Paolo Musumeci, Jiaqi Zhai, Bill Zhu, Hong Yan, Srihari Reddy. 778-780 [doi]
• Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity BiasVenkata Harshit Koneru, Xenija Neufeld, Sebastian Loth, Andreas Grün. 781-783 [doi]
• Entity-Aware Collections Ranking: A Joint Scoring ApproachSihao Chen, Sheng Li, Youhe Chen, Dong Yang. 784-786 [doi]
• Explore versus repeat: insights from an online supermarketMariagiorgia Agnese Tandoi, Daniela Solis Morales. 787-789 [doi]
• Improving Data Efficiency for Recommenders and LLMsNoveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, James Caverlee, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng. 790-792 [doi]
• Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)Moumita Bhattacharya, Vito Ostuni, Sudarshan Lamkhede. 793-795 [doi]
• Leveraging LLM generated labels to reduce bad matches in job recommendationsYingchi Pei, Yi Wei Pang, Warren Cai, Nilanjan Sengupta, Dheeraj Toshniwal. 796-799 [doi]
• LyricLure: Mining Catchy Hooks in Song Lyrics to Enhance Music Discovery and RecommendationSiddharth Sharma, Akshay Shukla, Ajinkya Walimbe, Tarun Sharma, Joaquin Delgado. 800-802 [doi]
• Off-Policy Selection for Optimizing Ad Display Timing in Mobile Games (Samsung Instant Plays)Katarzyna Siudek-Tkaczuk, Slawomir Kapka, Jedrzej Alchimowicz, Bartlomiej Swoboda, Michal Romaniuk. 803-805 [doi]
• Optimizing for Participation in Recommender SystemYuan Shao, Bibang Liu, Sourabh Bansod, Arnab Bhadury, Mingyan Gao, Yaping Zhang. 806-808 [doi]
• Pareto Front Approximation for Multi-Objective Session-Based Recommender SystemsTimo Wilm, Philipp Normann, Felix Stepprath. 809-812 [doi]
• Playlist Search Reinvented: LLMs Behind the CurtainGeetha Sai Aluri, Siddharth Sharma, Tarun Sharma, Joaquin Delgado. 813-815 [doi]
• Powerful A/B-Testing Metrics and Where to Find ThemOlivier Jeunen, Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko. 816-818 [doi]
• Privacy Preserving Conversion Modeling in Data Clean RoomKungang Li, Xiangyi Chen, Ling Leng, Jiajing Xu, Jiankai Sun, Behnam Rezaei. 819-822 [doi]
• Ranking Across Different Content Types: The Robust Beauty of Multinomial BlendingJan Malte Lichtenberg, Giuseppe Di Benedetto, Matteo Ruffini. 823-825 [doi]
• Scale-Invariant Learning-to-RankAlessio Petrozziello, Christian Sommeregger, Ye-Sheen Lim. 826-828 [doi]
• Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale RecommendersYin Zhang 0011, Ruoxi Wang, Xiang Li, Tiansheng Yao, Andrew Evdokimov, Jonathan Valverde, Yuan Gao, Jerry Zhang, Evan Ettinger, Ed H. Chi, Derek Zhiyuan Cheng. 829-831 [doi]
• Short-form Video Needs Long-term Interests: An Industrial Solution for Serving Large User Sequence ModelsYuening Li, Diego Uribe, Chuan He, Jiaxi Tang, Qingyun Liu, Junjie Shan, Ben Most, Kaushik Kalyan, Shuchao Bi, Xinyang Yi, Lichan Hong, Ed H. Chi, Liang Liu. 832-834 [doi]
• Sliding Window Training - Utilizing Historical Recommender Systems Data for Foundation ModelsSwanand Joshi, Yesu Feng, Ko-Jen Hsiao, Zhe Zhang, Sudarshan Lamkhede. 835-837 [doi]
• Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-trainingYi-Ping Hsu, Po-Wei Wang, Chantat Eksombatchai, Jiajing Xu. 838-840 [doi]
• Toward 100TB Recommendation Models with Embedding OffloadingIntaik Park, Ehsan Ardestani, Damian Reeves, Sarunya Pumma, Henry Tsang, Levy Zhao, Jian He, Joshua Deng, Dennis Van Der Staay, Yu Guo, Paul Zhang. 841-843 [doi]
• Towards Understanding The Gaps of Offline And Online Evaluation Metrics: Impact of Series vs. Movie RecommendationsBora Edizel, Tim Sweetser, Ashok Chandrashekar, Kamilia Ahmadi, Puja Das. 844-846 [doi]
• Why the Shooting in the Dark Method Dominates Recommender Systems PracticeDavid Rohde. 847-849 [doi]
• MAWI Rec: Leveraging Severe Weather Data in RecommendationBrendan Andrew Duncan, Surya Kallumadi, Taylor Berg-Kirkpatrick, Julian J. McAuley. 850-854 [doi]
• Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start RecommendationGenki Kusano. 861-865 [doi]
• Towards Green Recommender Systems: Investigating the Impact of Data Reduction on Carbon Footprint and Algorithm PerformancesGiuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano, Giovanni Semeraro. 866-871 [doi]
• LLMs for User Interest Exploration in Large-scale Recommendation SystemsJianling Wang, Haokai Lu, Yifan Liu, He Ma, Yueqi Wang, Yang Gu, Shuzhou Zhang, Ningren Han, Shuchao Bi, Lexi Baugher, Ed H. Chi, Minmin Chen. 872-877 [doi]
• Δ-OPE: Off-Policy Estimation with Pairs of PoliciesOlivier Jeunen, Aleksei Ustimenko. 878-883 [doi]
• It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music RecommendationAndres Ferraro, Michael D. Ekstrand, Christine Bauer 0001. 884-889 [doi]
• Pay Attention to Attention for Sequential RecommendationYuli Liu, Min Liu, Xiaojing Liu. 890-895 [doi]
• Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-CommerceYuan Wang, Zhiyu Li, Changshuo Zhang, Sirui Chen, Xiao Zhang 0034, Jun Xu 0001, Quan Lin. 896-901 [doi]
• Multi-Behavioral Sequential RecommendationShereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme. 902-906 [doi]
• MODEM: Decoupling User Behavior for Shared-Account Video Recommendations on Large Screen DevicesJiang Li, Zhen Zhang, Xiang Feng, Muyang Li, Yongqi Liu, Lantao Hu. 907-911 [doi]
• Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of ItemsAleksandr Vladimirovich Petrov, Craig Macdonald, Nicola Tonellotto. 912-917 [doi]
• Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in RecommendationLanling Xu, Zihan Lin, Jinpeng Wang 0001, Sheng Chen, Wayne Xin Zhao, Ji-Rong Wen. 918-923 [doi]
• CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation FrameworkFrancis Zac dela Cruz, Flora D. Salim, Yonchanok Khaokaew, Jeffrey Chan. 924-928 [doi]
• GLAMOR: Graph-based LAnguage MOdel embedding for citation RecommendationZafar Ali, Guilin Qi, Irfan Ullah 0001, Adam A. Q. Mohammed, Pavlos Kefalas, Khan Muhammad 0001. 929-933 [doi]
• Comparative Analysis of Pretrained Audio Representations in Music Recommender SystemsYan-Martin Tamm, Anna Aljanaki. 934-938 [doi]
• Positive-Sum Impact of Multistakeholder Recommender Systems for Urban Tourism Promotion and User UtilityPavel Merinov, Francesco Ricci 0001. 939-944 [doi]
• A Dataset for Adapting Recommender Systems to the Fashion Rental EconomyKarl Audun Kagnes Borgersen, Morten Goodwin, Morten Grundetjern, Jivitesh Sharma. 945-950 [doi]
• Societal Sorting as a Systemic Risk of RecommendersLuke Thorburn, Maria Polukarov, Carmine Ventre. 951-956 [doi]
• Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved RecommendationGeon Lee, KyungHo Kim, Kijung Shin. 957-962 [doi]
• Calibrating the Predictions for Top-N RecommendationsMasahiro Sato. 963-968 [doi]
• CoST: Contrastive Quantization based Semantic Tokenization for Generative RecommendationJieming Zhu, Mengqun Jin, Qijiong Liu, Zexuan Qiu, Zhenhua Dong, Xiu Li 0001. 969-974 [doi]
• On Interpretability of Linear AutoencodersMartin Spisák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peska. 975-980 [doi]
• Self-Attentive Sequential Recommendations with Hyperbolic RepresentationsEvgeny Frolov, Tatyana Matveeva, Leyla Mirvakhabova, Ivan V. Oseledets. 981-986 [doi]
• Evaluation and simplification of text difficulty using LLMs in the context of recommending texts in French to facilitate language learningHenri Jamet, Maxime Manderlier, Yash Raj Shrestha, Michalis Vlachos. 987-992 [doi]
• Fairness Matters: A look at LLM-generated group recommendationsAntonela Tommasel. 993-998 [doi]
• It's (not) all about that CTR: A Multi-Stakeholder Perspective on News Recommender MetricsHanne Vandenbroucke, Annelien Smets. 999-1003 [doi]
• Learned Ranking Function: From Short-term Behavior Predictions to Long-term User SatisfactionYi Wu, Daryl Chang, Jennifer She, Zhe Zhao 0001, Li Wei, Lukasz Heldt. 1004-1009 [doi]
• EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based RecommendationsChiyu Zhang, Yifei Sun, Minghao Wu, Jun Chen, Jie Lei, Muhammad Abdul-Mageed, Rong Jin, Angli Liu, Ji Zhu, Sem Park, Ning Yao, Bo Long. 1010-1015 [doi]
• Knowledge-Enhanced Multi-Behaviour Contrastive Learning for Effective RecommendationZeyuan Meng, Zixuan Yi, Iadh Ounis. 1016-1021 [doi]
• Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender SystemsOleg Lesota, Jonas Geiger, Max Walder, Dominik Kowald, Markus Schedl. 1022-1027 [doi]
• Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningPavan Seshadri, Shahrzad Shashaani, Peter Knees. 1028-1032 [doi]
• One-class recommendation systems with the hinge pairwise distance loss and orthogonal representationsRamin Raziperchikolaei, Young-joo Chung. 1033-1038 [doi]
• Better Generalization with Semantic IDs: A Case Study in Ranking for RecommendationsAnima Singh, Trung Vu, Nikhil Mehta 0002, Raghunandan H. Keshavan, Maheswaran Sathiamoorthy, Yilin Zheng, Lichan Hong, Lukasz Heldt, Li Wei, Devansh Tandon, Ed H. Chi, Xinyang Yi. 1039-1044 [doi]
• Neighborhood-Based Collaborative Filtering for Conversational RecommendationZhouhang Xie, Junda Wu, Hyunsik Jeon, Zhankui He, Harald Steck, Rahul Jha, Dawen Liang, Nathan Kallus, Julian J. McAuley. 1045-1050 [doi]
• Recommending Personalised Targeted Training Adjustments for Marathon RunnersCiara Feely, Brian Caulfield 0001, Aonghus Lawlor, Barry Smyth. 1051-1056 [doi]
• Recommending Healthy and Sustainable Meals exploiting Food Retrieval and Large Language ModelsAlessandro Petruzzelli, Cataldo Musto, Michele Ciro Di Carlo, Giovanni Tempesta, Giovanni Semeraro. 1057-1061 [doi]
• Can Editorial Decisions Impair Journal Recommendations? Analysing the Impact of Journal Characteristics on Recommendation SystemsElias Entrup, Ralph Ewerth, Anett Hoppe. 1062-1066 [doi]
• Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential RecommendationsAnton Klenitskiy, Anna Volodkevich, Anton Pembek, Alexey Vasilev. 1067-1072 [doi]
• Democratizing Urban Mobility Through an Open-Source, Multi-Criteria Route Recommendation SystemAlexander Eggerth, Javier Argota Sánchez-Vaquerizo, Dirk Helbing, Sachit Mahajan. 1073-1078 [doi]
• KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in RecommendationGiacomo Balloccu, Ludovico Boratto, Gianni Fenu, Mirko Marras, Alessandro Soccol. 1079-1084 [doi]
• Informed Dataset Selection with 'Algorithm Performance Spaces'Joeran Beel, Lukas Wegmeth, Lien Michiels, Steffen Schulz 0004. 1085-1090 [doi]
• Is It Really Complementary? Revisiting Behavior-based Labels for Complementary RecommendationKai Sugahara, Chihiro Yamasaki, Kazushi Okamoto. 1091-1095 [doi]
• Social Choice for Heterogeneous Fairness in RecommendationAmanda Aird, Elena Stefancova, Cassidy All, Amy Voida, Martin Homola, Nicholas Mattei, Robin Burke. 1096-1101 [doi]
• beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender SystemsVojtech Vancura, Pavel Kordík, Milan Straka. 1102-1107 [doi]
• Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender SystemsThi Ngoc Trang Tran, Seda Polat Erdeniz, Alexander Felfernig, Sebastian Lubos, Merfat El Mansi, Viet Man Le. 1108-1112 [doi]
• Are We Explaining the Same Recommenders? Incorporating Recommender Performance for Evaluating ExplainersAmir Reza Mohammadi, Andreas Peintner, Michael Müller, Eva Zangerle. 1113-1118 [doi]
• TLRec: A Transfer Learning Framework to Enhance Large Language Models for Sequential Recommendation TasksJiaye Lin, Shuang Peng, Zhong Zhang, Peilin Zhao. 1119-1124 [doi]
• Understanding Fairness in Recommender Systems: A Healthcare PerspectiveVeronica Kecki, Alan Said. 1125-1130 [doi]
• Exploratory Analysis of Recommending Urban Parks for Health-Promoting ActivitiesLinus W. Dietz, Sanja Scepanovic, Ke Zhou 0003, Daniele Quercia. 1131-1135 [doi]
• Leveraging Monte Carlo Tree Search for Group RecommendationAntonela Tommasel, J. Andres Diaz-Pace. 1136-1141 [doi]
• User Knowledge Prompt for Sequential RecommendationYuuki Tachioka. 1142-1146 [doi]
• Balancing Habit Repetition and New Activity Exploration: A Longitudinal Micro-Randomized Trial in Physical Activity RecommendationsIne Coppens, Toon De Pessemier, Luc Martens. 1147-1151 [doi]
• Exploring Coresets for Efficient Training and Consistent Evaluation of Recommender SystemsZheng Ju, Honghui Du, Elias Z. Tragos, Neil Hurley, Aonghus Lawlor. 1152-1157 [doi]
• What to compare? Towards understanding user sessions on price comparison platformsAhmadou Wagne, Julia Neidhardt. 1158-1162 [doi]
• Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback DatasetsLukas Wegmeth, Tobias Vente, Joeran Beel. 1163-1167 [doi]
• Enhancing Sequential Music Recommendation with Personalized Popularity AwarenessDavide Abbattista, Vito Walter Anelli, Tommaso Di Noia, Craig Macdonald, Aleksandr Vladimirovich Petrov. 1168-1173 [doi]
• Rs4rs: Semantically Find Recent Publications from Top Recommendation System-Related VenuesTri Kurniawan Wijaya, Edoardo D'Amico, Gábor Fodor, Manuel V. Loureiro. 1174-1176 [doi]
• GenUI(ne) CRS: UI Elements and Retrieval-Augmented Generation in Conversational Recommender Systems with LLMsUlysse Maes, Lien Michiels, Annelien Smets. 1177-1179 [doi]
• Multi-Preview Recommendation via Reinforcement LearningYang Xu, Kuan-Ting Lai, PengCheng Xiong, Zhong Wu. 1180-1183 [doi]
• A Tool for Explainable Pension Fund Recommendations using Large Language ModelsEduardo Alves da Silva, Leandro Balby Marinho, Edleno Silva de Moura, Altigran Soares da Silva. 1184-1186 [doi]
• Stalactite: toolbox for fast prototyping of vertical federated learning systemsAnastasiia Zakharova, Dmitriy Alexandrov, Maria Khodorchenko, Nikolay Butakov, Alexey Vasilev, Maxim Savchenko, Alexander Grigorievskiy. 1187-1190 [doi]
• RePlay: a Recommendation Framework for Experimentation and Production UseAlexey Vasilev, Anna Volodkevich, Denis Kulandin, Tatiana Bysheva, Anton Klenitskiy. 1191-1194 [doi]
• RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News RecommendationsJohannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava 0004, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen. 1195-1199 [doi]
• FAccTRec 2024: The 7th Workshop on Responsible RecommendationMichael D. Ekstrand, Toshihiro Kamishima, Amifa Raj, Karlijn Dinnissen. 1200-1201 [doi]
• MuRS 2024: 2nd Music Recommender Systems WorkshopAndres Ferraro, Lorenzo Porcaro, Peter Knees, Christine Bauer 0001. 1202-1205 [doi]
• CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender SystemsOlivier Jeunen, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang. 1206-1209 [doi]
• SURE 2024: Workshop on Strategic and Utility-aware REcommendationHiman Abdollahpouri, Tonia Danylenko, Masoud Mansoury, Babak Loni, Daniel Russo 0001, Mihajlo Grbovic. 1210-1212 [doi]
• VideoRecSys + LargeRecSys 2024Khushhall Chandra Mahajan, Amey Porobo Dharwadker, Saurabh Gupta, Brad Schumitsch, Arnab Bhadury, Ding Tong, Ko-Jen Hsiao, Liang Liu. 1213-1215 [doi]
• AltRecSys: A Workshop on Alternative, Unexpected, and Critical Ideas in RecommendationMichael D. Ekstrand, Maria Soledad Pera, Alan Said. 1216-1218 [doi]
• Workshop on Context-Aware Recommender Systems (CARS) 2024Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger. 1219-1221 [doi]
• Fourth Workshop on Recommender Systems for Human Resources (RecSys in HR 2024)Toine Bogers, David Graus, Mesut Kaya, Chris Johnson 0011, Jens-Joris Decorte, Tijl De Bie. 1222-1226 [doi]
• RecTemp: Temporal Reasoning in Recommendation SystemsAdir Solomon, Tsvi Kuflik, Bracha Shapira, Ido Guy. 1227-1228 [doi]
• Workshop on Recommenders in Tourism (RecTour) 2024Julia Neidhardt, Tsvi Kuflik, Amit Livne, Markus Zanker. 1229-1231 [doi]
• The 6th International Workshop on Health Recommender SystemsHanna Hauptmann, Christoph Trattner, Helma Torkamaan. 1232-1236 [doi]
• Reflections on Recommender Systems: Past, Present, and Future (INTROSPECTIVES)Alan Said, Christine Bauer 0001, Eva Zangerle. 1237-1238 [doi]
• First International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2024)Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, Francesco Ricci 0001. 1239-1241 [doi]
• NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender SystemsAlain Starke, Sanne Vrijenhoek, Lien Michiels, Johannes Kruse, Nava Tintarev. 1242-1244 [doi]
• Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Vito Walter Anelli, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone, Markus Zanker. 1245-1249 [doi]
• The 1st International Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommendation (ROEGEN)Yashar Deldjoo, Julian J. McAuley, Scott Sanner, Pablo Castells, Shuai Zhang 0007, Enrico Palumbo. 1250-1252 [doi]
• 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'24)Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen. 1253-1257 [doi]
• 12th International Workshop on News Recommendation and Analytics (INRA'24)Benjamin Kille, Andreas Lommatzsch, Célina Treuillier, Vandana Yadav, Özlem Özgöbek. 1258-1261 [doi]
• EARL: Workshop on Evaluating and Applying Recommendation Systems with Large Language ModelsIrene Li, Ruihai Dong, Lei Li 0042, Li Chen 0009. 1262-1264 [doi]
• RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender SystemsValerio Guarrasi, Federico Siciliano, Fabrizio Silvestri. 1265-1269 [doi]
• Deep Recommendation using GraphsPanagiotis Symeonidis. 1270-1271 [doi]
• Conducting User Experiments in Recommender SystemsBart P. Knijnenburg, Edward C. Malthouse. 1272-1273 [doi]
• Computational Methods for Designing Human-Centered Recommender Systems: A Case Study Approach Intersecting Visual Arts and HealthcareBereket Abera Yilma. 1274-1276 [doi]
• Conducting Recommender Systems User Studies Using POPROXRobin Burke, Joseph A. Konstan, Michael D. Ekstrand. 1277-1278 [doi]
• Economics of Recommender SystemsEmilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, Sergio Pastorello. 1279-1280 [doi]
• A Tutorial on Feature Interpretation in Recommender SystemsZhaocheng Du, Chuhan Wu, Qinglin Jia, Jieming Zhu, Xu Chen 0017. 1281-1282 [doi]
• Bridging Viewpoints in News with Recommender SystemsJia Hua Jeng. 1283-1289 [doi]
• Multimodal Representation Learning for High-Quality Recommendations in Cold-Start and Beyond-AccuracyMarta Moscati. 1290-1295 [doi]
• Supporting Knowledge Workers through Personal Information Assistance with Context-aware Recommender SystemsMahta Bakhshizadeh. 1296-1301 [doi]
• Evaluating the Pros and Cons of Recommender Systems ExplanationsKathrin Wardatzky. 1302-1307 [doi]
• AI-based Human-Centered Recommender Systems: Empirical Experiments and Research InfrastructureRuixuan Sun. 1308-1313 [doi]
• Integrating Matrix Factorization with Graph based ModelsRachana Mehta. 1314-1317 [doi]
• Explainable Multi-Stakeholder Job Recommender SystemsRoan Schellingerhout. 1318-1322 [doi]
• CEERS: Counterfactual Evaluations of Explanations in Recommender SystemsMikhail Baklanov. 1323-1329 [doi]
• Towards Sustainable Recommendations in Urban TourismPavel Merinov. 1330-1334 [doi]
• How to Evaluate Serendipity in Recommender Systems: the Need for a SerendiptionnaireBrett Binst. 1335-1341 [doi]
• Personal Values and Community-Centric Environmental Recommender Systems: Enhancing Sustainability Through User EngagementBianca Maria Deconcini. 1342-1347 [doi]
• Enhancing Privacy in Recommender Systems through Differential Privacy TechniquesAngela Di Fazio. 1348-1352 [doi]
• Fairness Explanations in Recommender SystemsLuan Soares de Souza. 1353-1354 [doi]
• Learning Personalized Health Recommendations via Offline Reinforcement LearningLarry Donald Preuett. 1355-1357 [doi]
• Explainable and Faithful Educational Recommendations through Causal Language Modelling via Knowledge GraphsNeda Afreen. 1358-1360 [doi]
• Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation SystemsAlessandro Petruzzelli. 1361-1367 [doi]
• Fairness and Transparency in Music Recommender Systems: Improvements for ArtistsKarlijn Dinnissen. 1368-1375 [doi]
• Bias in Book RecommendationSavvina Daniil. 1376-1381 [doi]
• A New Perspective in Health Recommendations: Integration of Human Pose EstimationGaetano Dibenedetto. 1382-1387 [doi]
• Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy MeasuresThomas Elmar Kolb. 1388-1394 [doi]
• Explainability in Music Recommender SystemShahrzad Shashaani. 1395-1401 [doi]