Factorization Machines(FM)produce a single fixed representation of each feature in different input instances,ignoring the multi-semantic nature of features and limiting the representation and predic-tion ability of Click-through Rate(CTR)estimation models.To solve this problem,a factor decomposi-tion machine model of deep collaborative perception is proposed,which introduces multi-semantic interac-tion perception network and triple input perception network,and helps to learn the perception factors of different samples through multi-semantic feature domain interaction and fusion of feature interaction infor-mation at different levels,so as to obtain more accurate feature representation.Through model comparison experiment and ablation experiment,this model can effectively improve the accuracy of click-through rate prediction.
NETL
NSTL
万方数据
维普
