Created
June 24, 2020 15:13
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def bpr_mf(user_count, item_count, hidden_dim): | |
u = tf.placeholder(tf.int32, [None]) | |
i = tf.placeholder(tf.int32, [None]) | |
j = tf.placeholder(tf.int32, [None]) | |
with tf.device("/cpu:0"): | |
user_emb_w = tf.get_variable("user_emb_w", [user_count+1, hidden_dim], | |
initializer=tf.random_normal_initializer(0, 0.1)) | |
item_emb_w = tf.get_variable("item_emb_w", [item_count+1, hidden_dim], | |
initializer=tf.random_normal_initializer(0, 0.1)) | |
item_b = tf.get_variable("item_b", [item_count+1, 1], | |
initializer=tf.constant_initializer(0.0)) | |
u_emb = tf.nn.embedding_lookup(user_emb_w, u) | |
i_emb = tf.nn.embedding_lookup(item_emb_w, i) | |
i_b = tf.nn.embedding_lookup(item_b, i) | |
j_emb = tf.nn.embedding_lookup(item_emb_w, j) | |
j_b = tf.nn.embedding_lookup(item_b, j) | |
# MF predict: u_i > u_j | |
x = i_b - j_b + tf.reduce_sum(tf.mul(u_emb, (i_emb - j_emb)), 1, keep_dims=True) | |
mf_auc = tf.reduce_mean(tf.to_float(x > 0)) | |
l2_norm = tf.add_n([ | |
tf.reduce_sum(tf.mul(u_emb, u_emb)), | |
tf.reduce_sum(tf.mul(i_emb, i_emb)), | |
tf.reduce_sum(tf.mul(j_emb, j_emb)) | |
]) | |
regulation_rate = 0.0001 | |
bprloss = regulation_rate * l2_norm - tf.reduce_mean(tf.log(tf.sigmoid(x))) | |
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(bprloss) | |
return u, i, j, mf_auc, bprloss, train_op |
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