Feature Columns
SparseFeat is a namedtuple with signature SparseFeat(name, vocabulary_size, embedding_dim, use_hash, dtype,embedding_name, group_name)
vocabulary_size : number of unique feature values for sprase feature or hashing space when use_hash=True
embedding_dim : embedding dimension
use_hash : defualt False.If True the input will be hashed to space of size vocabulary_size.
dtype : default float32.dtype of input tensor.
embedding_name : default None. If None, the embedding_name will be same as name.
group_name : feature group of this feature.
DenseFeat is a namedtuple with signature DenseFeat(name, dimension, dtype)
dimension : dimension of dense feature vector.
dtype : default float32.dtype of input tensor.
VarLenSparseFeat
VarLenSparseFeat is a namedtuple with signature VarLenSparseFeat(sparsefeat, maxlen, combiner, length_name, weight_name,weight_norm)
sparsefeat : a instance of SparseFeat
maxlen : maximum length of this feature for all samples
combiner : pooling method,can be sum,mean or max
length_name : feature length name,if None, value 0 in feature is for padding.
weight_name : default None. If not None, the sequence feature will be multiplyed by the feature whose name is weight_name.
weight_norm : default True. Whether normalize the weight score or not.
FM (Convolutional Click Prediction Model)
FM Model API
Factorization Machines
DSSM (Deep Structured Semantic Model)
DSSM Model API
Deep Structured Semantic Models for Web Search using Clickthrough Data
YoutubeDNN Model API
Deep Neural Networks for YouTube Recommendations
NCF (Neural Collaborative Filtering)
NCF Model API
Neural Collaborative Filtering
MIND (Multi-Interest Network with Dynamic routing)
MIND Model API
Multi-interest network with dynamic routing for recommendation at Tmall
