1 citations · 1 across the 6 of their papers we have counts for
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TokenMixer-Large: Scaling Up Large Ranking Models in Industrial Recommenders
Yuchen Jiang, Jie Zhu, Xintian Han +18
While scaling laws for recommendation models have gained significant traction, existing architectures such as Wukong, HiFormer and DHEN, often struggle with sub-optimal designs and…
LEMUR: Large scale End-to-end MUltimodal Recommendation
Xintian Han, Honggang Chen, Quan Lin +14
Traditional ID-based recommender systems often struggle with cold-start and generalization challenges. Multimodal recommendation systems, which leverage textual and visual data, of…
RankMixer: Scaling Up Ranking Models in Industrial Recommenders
Jie Zhu, Zhifang Fan, Xiaoxie Zhu +18
Recent progress on large language models (LLMs) has spurred interest in scaling up recommendation systems, yet two practical obstacles remain. First, training and serving cost on i…
Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever
Xingyan Bin, Jianfei Cui, Wujie Yan +7
Retrievers, which form one of the most important recommendation stages, are responsible for efficiently selecting possible positive samples to the later stages under strict latency…