| 2024 | CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation. | Yaoyiran Li, Xiang Zhai, Moustafa Alzantot, Keyi Yu, Ivan Vulic, Anna Korhonen, Mohamed Hammad |
| 2024 | Embedding Optimization for Training Large-scale Deep Learning Recommendation Systems with EMBark. | Shijie Liu, Nan Zheng, Hui Kang, Xavier Simmons, Junjie Zhang, Matthias Langer, Wenjing Zhu, Minseok Lee, Zehuan Wang |
| 2024 | Pay Attention to Attention for Sequential Recommendation. | Yuli Liu, Min Liu, Xiaojing Liu |
| 2024 | Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space. | Yu Liu, Qinglin Jia, Shuting Shi, Chuhan Wu, Zhaocheng Du, Zheng Xie, Ruiming Tang, Muyu Zhang, Ming Li |
| 2024 | Short-form Video Needs Long-term Interests: An Industrial Solution for Serving Large User Sequence Models. | Yuening 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 |
| 2024 | FairCRS: Towards User-oriented Fairness in Conversational Recommendation Systems. | Qin Liu, Xuan Feng, Tianlong Gu, Xiaoli Liu |
| 2024 | Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery. | Jiayu Li, Aixin Sun, Weizhi Ma, Peijie Sun, Min Zhang |
| 2024 | TLRec: A Transfer Learning Framework to Enhance Large Language Models for Sequential Recommendation Tasks. | Jiaye Lin, Shuang Peng, Zhong Zhang, Peilin Zhao |
| 2024 | Country-diverted experiments for mitigation of network effects. | Lina Lin, Changping Meng, Jennifer Brennan, Jean Pouget-Abadie, Ningren Han, Shuchao Bi, Yajun Peng |
| 2024 | Encouraging Exploration in Spotify Search through Query Recommendations. | Henrik Lindstrom, Humberto Jess Corona Pampn, Enrico Palumbo, Alva Liu |
| 2024 | Bootstrapping Conditional Retrieval for User-to-Item Recommendations. | Hongtao Lin, Haoyu Chen, Jaewon Yang, Jiajing Xu |
| 2024 | Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential Recommendation. | Weixin Li, Xiaolin Lin, Weike Pan, Zhong Ming |
| 2024 | ReChorus2.0: A Modular and Task-Flexible Recommendation Library. | Jiayu Li, Hanyu Li, Zhiyu He, Weizhi Ma, Peijie Sun, Min Zhang, Shaoping Ma |
| 2024 | EARL: Workshop on Evaluating and Applying Recommendation Systems with Large Language Models. | Irene Li, Ruihai Dong, Lei Li, Li Chen |
| 2024 | Privacy Preserving Conversion Modeling in Data Clean Room. | Kungang Li, Xiangyi Chen, Ling Leng, Jiajing Xu, Jiankai Sun, Behnam Rezaei |
| 2024 | Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending. | Jan Malte Lichtenberg, Giuseppe Di Benedetto, Matteo Ruffini |
| 2024 | Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems. | Oleg Lesota, Jonas Geiger, Max Walder, Dominik Kowald, Markus Schedl |
| 2024 | Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation. | Geon Lee, Kyungho Kim, Kijung Shin |
| 2024 | Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation. | Genki Kusano |
| 2024 | EB-NeRD a large-scale dataset for news recommendation. | Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen |
| 2024 | RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. | Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen |
| 2024 | Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity Bias. | Venkata Harshit Koneru, Xenija Neufeld, Sebastian Loth, Andreas Grn |
| 2024 | Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures. | Thomas Elmar Kolb |
| 2024 | Conducting User Experiments in Recommender Systems. | Bart P. Knijnenburg, Edward C. Malthouse |
| 2024 | Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations. | Anton Klenitskiy, Anna Volodkevich, Anton Pembek, Alexey Vasilev |