def item_graph(subgraph):
_, subgraph['seq_vec'] = embedding_layer.apply(
l2_reg=l2_reg_embed,
init='normal',
id_=subgraph['seq_item_id'],
shape=[total_items, dim_item_embed],
subgraph=subgraph,
scope='item')
def train_itemgraph(subgraph):
_, item_embedded_tensor_1 = embedding_layer.apply(l2_reg=l2_reg,
init="normal",
id_=subgraph["item_id_1"],
shape=[total_item_num, embedding_dim],
subgraph=subgraph,
scope="ItemEmbedding")
_, item_embedded_tensor_2 = embedding_layer.apply(l2_reg=l2_reg,
init="normal",
id_=subgraph["item_id_2"],
shape=[total_item_num, embedding_dim],
subgraph=subgraph,
scope="ItemEmbedding")
subgraph["item_vec_1"] = concatenate.apply([
subgraph["item_meta_vec_1"], subgraph["item_stat_vec_1"], item_embedded_tensor_1])
subgraph["item_vec_2"] = concatenate.apply([
subgraph["item_meta_vec_2"], subgraph["item_stat_vec_2"], item_embedded_tensor_2])
pass
def serve_itemgraph(subgraph):
_, item_embedded_tensor = embedding_layer.apply(l2_reg=l2_reg,
init="normal",
id_=subgraph["item_id"],
shape=[total_item_num, embedding_dim],
subgraph=subgraph,
scope="ItemEmbedding")
subgraph["item_vec"] = concatenate.apply([
subgraph["item_meta_vec"], subgraph["item_stat_vec"], item_embedded_tensor])
pass
def usergraph(subgraph):
_, item_embedded_tensor = embedding_layer.apply(l2_reg=l2_reg,
init="normal",
id_=subgraph["user_history_vec"],
shape=[total_item_num, embedding_dim],
subgraph=subgraph,
scope="ItemEmbedding") # shaped [-1, fea_user_history_dim, embedding_dim]
user_history_repr = variable_average.apply(
sequence=item_embedded_tensor, seq_len=subgraph["user_history_len"]) # shaped [-1, 1, embedding_dim]
subgraph["user_vec"] = concatenate.apply([
subgraph["user_demography_vec"], subgraph["user_stat_vec"], user_history_repr])
pass