| 2024 | Towards Green Recommender Systems: Investigating the Impact of Data Reduction on Carbon Footprint and Algorithm Performances. | Giuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano, Giovanni Semeraro |
| 2024 | Fairness Explanations in Recommender Systems. | Luan Soares de Souza |
| 2024 | RecTemp: Temporal Reasoning in Recommendation Systems. | Adir Solomon, Tsvi Kuflik, Bracha Shapira, Ido Guy |
| 2024 | 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 |
| 2024 | Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations. | Anima Singh, Trung Vu, Nikhil Mehta, Raghunandan H. Keshavan, Maheswaran Sathiamoorthy, Yilin Zheng, Lichan Hong, Lukasz Heldt, Li Wei, Devansh Tandon, Ed H. Chi, Xinyang Yi |
| 2024 | A Tool for Explainable Pension Fund Recommendations using Large Language Models. | Eduardo Alves da Silva, Leandro Balby Marinho, Edleno Silva de Moura, Altigran Soares da Silva |
| 2024 | Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation. | Alex Shtoff, Michael Viderman, Naama Haramaty-Krasne, Oren Somekh, Ariel Raviv, Tularam Ban |
| 2024 | Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits. | Tatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, Yuta Saito |
| 2024 | Explainability in Music Recommender System. | Shahrzad Shashaani |
| 2024 | LyricLure: Mining Catchy Hooks in Song Lyrics to Enhance Music Discovery and Recommendation. | Siddharth Sharma, Akshay Shukla, Ajinkya Walimbe, Tarun Sharma, Joaquin Delgado |
| 2024 | Optimizing for Participation in Recommender System. | Yuan Shao, Bibang Liu, Sourabh Bansod, Arnab Bhadury, Mingyan Gao, Yaping Zhang |
| 2024 | Analyzing User Preferences and Quality Improvement on Bing's WebPage Recommendation Experience with Large Language Models. | Jaidev Shah, Gang Luo, Jialin Liu, Amey Barapatre, Fan Wu, Chuck Wang, Hongzhi Li |
| 2024 | Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning. | Pavan Seshadri, Shahrzad Shashaani, Peter Knees |
| 2024 | Explainable Multi-Stakeholder Job Recommender Systems. | Roan Schellingerhout |
| 2024 | Calibrating the Predictions for Top-N Recommendations. | Masahiro Sato |
| 2024 | Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization. | Abdulaziz Samra, Evgeny Frolov, Alexey Vasilev, Alexander Grigorevskiy, Anton Vakhrushev |
| 2024 | Reflections on Recommender Systems: Past, Present, and Future (INTROSPECTIVES). | Alan Said, Christine Bauer, Eva Zangerle |
| 2024 | Improving Data Efficiency for Recommenders and LLMs. | Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, James Caverlee, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng |
| 2024 | Why the Shooting in the Dark Method Dominates Recommender Systems Practice. | David Rohde |
| 2024 | Leveraging User History with Transformers for News Clicking: The DArgk Approach. | Juan Manuel Rodriguez, Antonela Tommasel |
| 2024 | One-class recommendation systems with the hinge pairwise distance loss and orthogonal representations. | Ramin Raziperchikolaei, Young-joo Chung |
| 2024 | The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation. | Zekai Qu, Ruobing Xie, Chaojun Xiao, Zhanhui Kang, Xingwu Sun |
| 2024 | Learning Personalized Health Recommendations via Offline Reinforcement Learning. | Larry Donald Preuett |
| 2024 | Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations. | Alessandro Petruzzelli, Cataldo Musto, Lucrezia Laraspata, Ivan Rinaldi, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro |
| 2024 | Recommending Healthy and Sustainable Meals exploiting Food Retrieval and Large Language Models. | Alessandro Petruzzelli, Cataldo Musto, Michele Ciro Di Carlo, Giovanni Tempesta, Giovanni Semeraro |