def _create_loss(self):
with tf.name_scope("loss"):
# loss for L(Theta)
p1, q1,b1,self.output= self._create_inference(self.item_input_pos)
_, q2,b2, output_social = self._create_inference(self.item_input_social)
_, q3,b3, output_neg = self._create_inference(self.item_input_neg)
result1 = tf.divide(self.output - output_social,self.suk)
result2 = output_social - output_neg
self.loss = learner.pairwise_loss(self.loss_function,result1)+learner.pairwise_loss(self.loss_function,result2)+ self.reg_mf * ( tf.reduce_sum(tf.square(p1)) \
+ tf.reduce_sum(tf.square(q2)) + tf.reduce_sum(tf.square(q1))+tf.reduce_sum(tf.square(q3))+tf.reduce_sum(tf.square(b1))+tf.reduce_sum(tf.square(b2))+tf.reduce_sum(tf.square(b3)))
def _create_loss(self):
with tf.name_scope("loss"):
# BPR loss for L(Theta)
self.p1, self.q1, self.output = self._create_inference(self.item_input_pos)
self.p2, self.q2, self.output_neg = self._create_inference(self.item_input_neg)
self.result = self.output - self.output_neg
self.loss = learner.pairwise_loss(self.loss_function, self.result)########
self.opt_loss = self.loss + self.lambda_bilinear * ( tf.reduce_sum(tf.square(self.p1)) \
+ tf.reduce_sum(tf.square(self.q2)) + tf.reduce_sum(tf.square(self.q1)))\
+ self.gamma_bilinear * self._regular([(self.W, self.b)]) \
+ self.lambda_weight * (self._regular(self.P) + self._regular([(self.W, self.b)]))
def _create_loss(self):
with tf.name_scope("loss"):
p1, q1, self.output = self._create_inference(self.item_input)
if self.ispairwise == True:
_, q2, self.output_neg = self._create_inference(
self.item_input_neg)
result = self.output - self.output_neg
self.loss = learner.pairwise_loss(self.loss_function,result) + self.reg_mf * ( tf.reduce_sum(tf.square(p1)) \
+ tf.reduce_sum(tf.square(q2)) + tf.reduce_sum(tf.square(q1)))
else:
self.loss = learner.pointwise_loss(self.loss_function, self.lables,self.output)+self.reg_mlp * (tf.reduce_sum(tf.square(p1)) \
+ tf.reduce_sum(tf.square(q1)))
def _create_loss(self):
with tf.name_scope("loss"):
self.output = tf.reduce_sum(tf.multiply(self.u_embeddings,
self.pos_i_embeddings),
axis=1)
output_neg = tf.reduce_sum(tf.multiply(self.u_embeddings,
self.neg_j_embeddings),
axis=1)
regularizer = self.reg * ( tf.reduce_sum(tf.square(self.u_embeddings)) \
+ tf.reduce_sum(tf.square(self.pos_i_embeddings)) + tf.reduce_sum(tf.square(self.neg_j_embeddings)))
self.loss = learner.pairwise_loss(
self.loss_function, self.output - output_neg) + regularizer
def _create_loss(self):
with tf.name_scope("loss"):
p1, q1, self.output = self._create_inference(
self.user_input, self.item_input, self.num_idx)
if self.ispairwise == True:
_, q2, output_neg = self._create_inference(
self.user_input_neg, self.item_input_neg, self.num_idx_neg)
self.result = self.output - output_neg
self.loss = learner.pairwise_loss(self.loss_function,self.result) + self.lambda_bilinear * ( tf.reduce_sum(tf.square(p1))) \
+self.gamma_bilinear*(tf.reduce_sum(tf.square(q2)) + tf.reduce_sum(tf.square(q1)))
else:
self.loss = learner.pointwise_loss(self.loss_function, \
self.lables,self.output) + self.lambda_bilinear *\
(tf.reduce_sum(tf.square(p1)))+self.gamma_bilinear *(tf.reduce_sum(tf.square(q1)))
def _create_loss(self):
with tf.name_scope("loss"):
# loss for L(Theta)
UI_u, IU_i, IL_i, LI_l, self.output = self._create_inference(
self.item_input)
if self.ispairwise == True:
_, IU_j, IL_j, _, output_neg = self._create_inference(
self.item_input_neg)
self.result = self.output - output_neg
self.loss = learner.pairwise_loss(self.loss_function,self.result) + self.reg_mf * ( tf.reduce_sum(tf.square(UI_u)) \
+ tf.reduce_sum(tf.square(IU_i)) + tf.reduce_sum(tf.square(IL_i)) + tf.reduce_sum(tf.square(LI_l))+ \
tf.reduce_sum(tf.square(LI_l))+tf.reduce_sum(tf.square(IU_j))+tf.reduce_sum(tf.square(IL_j)))
else:
self.loss = learner.pointwise_loss(self.loss_function,self.lables,self.output) + self.reg_mf * (tf.reduce_sum(tf.square(UI_u)) \
+tf.reduce_sum(tf.square(IU_i))+ tf.reduce_sum(tf.square(IL_i))+tf.reduce_sum(tf.square(LI_l)))
