4 papers
Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design
Bojian Hou, Xiaolong Liu, Xiaoyi Liu +26
Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-s…
LLM Retrieval for Stable and Predictable Ad Recommendations
Vinodh Kumar Sunkara, Satheeshkumar Karuppusamy, Hangjun Xu +13
Traditional ads recommendation systems have primarily focused on optimizing for prediction accuracy of click or conversion events using canonical metrics such as recall or normaliz…
Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID
Carolina Zheng, Minhui Huang, Dmitrii Pedchenko +15
The exponential growth of online content has posed significant challenges to ID-based models in industrial recommendation systems, ranging from extremely high cardinality and dynam…
Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction
Han Xu, Taoxing Pan, Zhiqiang Liu +2
User behavior modeling -- which aims to extract user interests from behavioral data -- has shown great power in Click-through rate (CTR) prediction, a key component in recommendati…