def _construct_placeholders(self):
"""Create placeholders for our model"""
self.input_users = tf.placeholder(tf.int32, [None], 'UserID')
self.input_items = tf.placeholder(tf.int32, [None], 'ItemID')
self.input_items_negative = tf.placeholder(tf.int32, [None],
'NegativeItemID')
self.input_neighborhoods = tf.placeholder(tf.int32, [None, None],
'Neighborhood')
self.input_neighborhood_lengths = tf.placeholder(tf.int32, [None],
'NeighborhoodLengthID')
self.input_neighborhoods_negative = tf.placeholder(tf.int32,
[None, None],
'NeighborhoodNeg')
self.input_neighborhood_lengths_negative = tf.placeholder(tf.int32,
[None],
'NeighborhoodLengthIDNeg')
# Add our placeholders
add_to_collection(GraphKeys.PLACEHOLDER, [self.input_users,
self.input_items,
self.input_items_negative,
self.input_neighborhoods,
self.input_neighborhood_lengths,
self.input_neighborhoods_negative,
self.input_neighborhood_lengths_negative,
self.dropout])
def _construct_placeholders(self):
self.input_users = tf.placeholder(tf.int32, [None], 'UserID')
self.input_items = tf.placeholder(tf.int32, [None], 'ItemID')
self.input_items_negative = tf.placeholder(tf.int32, [None], 'NegativeItemID')
# Add our placeholders
add_to_collection(GraphKeys.PLACEHOLDER, [self.input_users,
self.input_items,
self.input_items_negative])
def _construct_placeholders(self):
self.input_users = tf.placeholder(tf.int32, [None], 'UserID')
self.input_items = tf.placeholder(tf.int32, [None], 'ItemID')
self.input_items_negative = tf.placeholder(tf.int32, [None],
'NegativeItemID')
# Add our placeholders
add_to_collection(
GraphKeys.PLACEHOLDER,
[self.input_users, self.input_items, self.input_items_negative])
def _construct_placeholders(self):
self.input_users = tf.placeholder(tf.int32, [None], 'UserID')
self.input_items = tf.placeholder(tf.int32, [None], 'ItemID')
# LC > popularity of positive items
self.input_positive_items_popularity = tf.placeholder(
tf.float32, [None], 'PositiveItemsPopularity')
self.input_items_negative = tf.placeholder(tf.int32, [None],
'NegativeItemID')
# LC > popularity of negative items
self.input_negative_items_popularity = tf.placeholder(
tf.float32, [None], 'NegativeItemsPopularity')
# Add our placeholders
add_to_collection(GraphKeys.PLACEHOLDER, [
self.input_users, self.input_items,
self.input_positive_items_popularity, self.input_items_negative,
self.input_negative_items_popularity
])
def _construct_placeholders(self):
"""Create placeholders for our model"""
self.input_users = tf.placeholder(tf.int32, [None], 'UserID')
self.input_items = tf.placeholder(tf.int32, [None], 'ItemID')
# LC > popularity of positive items
self.input_positive_items_popularity = tf.placeholder(
tf.float32, [None], 'PositiveItemsPopularity')
self.input_items_negative = tf.placeholder(tf.int32, [None],
'NegativeItemID')
# LC > popularity of negative items
self.input_negative_items_popularity = tf.placeholder(
tf.float32, [None], 'NegativeItemsPopularity')
self.input_neighborhoods = tf.placeholder(tf.int32, [None, None],
'Neighborhood')
self.input_neighborhood_lengths = tf.placeholder(
tf.int32, [None], 'NeighborhoodLengthID')
self.input_neighborhoods_negative = tf.placeholder(
tf.int32, [None, None], 'NeighborhoodNeg') # X
self.input_neighborhood_lengths_negative = tf.placeholder(
tf.int32, [None], 'NeighborhoodLengthIDNeg') # X
# Add our placeholders
add_to_collection(GraphKeys.PLACEHOLDER, [
self.input_users, self.input_items,
self.input_positive_items_popularity, self.input_items_negative,
self.input_negative_items_popularity, self.input_neighborhoods,
self.input_neighborhood_lengths, self.input_neighborhoods_negative,
self.input_neighborhood_lengths_negative, self.dropout
])