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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…
A Collaborative Ensemble Framework for CTR Prediction
Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu +13
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into…
InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
Zhichen Zeng, Xiaolong Liu, Mengyue Hang +25
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous informati…