3rd International Workshop on Industrial Recommendation Systems (IRS)
Luyi Ma, Xiaohan Li, Kamilia Ahmadi, Jianpeng Xu, Philip S. Yu, George Karypis · 2024
Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms, and social networks, to provide suggestions that users will most likely consume or connect, thus improving the user experience. This motivates people in industry and research organizations to focus on personalization and recommendation algorithms, resulting in many research papers. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment, including but not limited to business metric definition and evaluation, scalability, recommendation quality control, robustness, fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions to industrial recommendation systems. This workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue for industrial researchers to share practical insights and for academic researchers to become aware of the additional factors of algorithm adoption in real production systems.