BPR.py

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