特征融合与分发的多专家并行推荐算法框架

杨哲,葛洪伟,李婷

Framework of feature fusion and distribution with mixture of experts for parallel recommendation algorithm

Zhe YANG,Hong-wei GE,Ting LI

表 2 ME-DCN与其他SOTA模型在3个数据集上的性能比较

Tab.2 Performance comparisons between ME-DCN and other SOTA models in three datasets

模型 Criteo Avazu MovieLens-1M AUC LogLoss AUC LogLoss AUC LogLoss DeepFM 0.8007 0.4508 0.7852 0.3780 0.8932 0.3202 DCN 0.8099 0.4419 0.7905 0.3744 0.8935 0.3197 xDeepFM 0.8052 0.4418 0.7894 0.3794 0.8923 0.3251 AutoInt+ 0.8083 0.4434 0.7774 0.3811 0.8488 0.3753 DCN-v2 0.8115 0.4406 0.7907 0.3742 0.8964 0.3160 EDCN 0.8001 0.5415 0.7793 0.3803 0.8722 0.3469 CowClip 0.8097 0.4420 0.7906 0.3740 0.8961 0.3174 本文方法 0.8122 0.4398 0.7928 0.3732 0.8970 0.3163