使用Tensorflow的基于矩阵分解的推荐

Question

我是张量流的新手，正在探索使用张量流的推荐系统。我已经在github中验证了一些示例代码，并遇到了与下面的大致相同的以下内容

https://github.com/songgc/TF-recomm/blob/master/svd_train_val.py

但问题是，如何在上面的代码中为用户U1选择最佳推荐？

如果有任何示例代码或方法，请分享。感谢

Answer 1

这有点难！基本上，当svd返回时，它会关闭会话，张量会丢失其值(您仍然保留图形)。有几个选项：

1. 将模型保存到一个文件中，稍后再进行恢复
2. 不要将会话放在with tf.Session() as sess: ....块中，而是返回会话
3. 在with ...块内进行用户处理

最糟糕的选择是选项3：你应该单独训练你的模型，而不是使用它。最好的方法是把你的模型和权重保存在某个地方，然后恢复会话。然而，一旦恢复会话对象，您仍然需要解决如何使用它的问题。为了演示这一部分，我将使用选项3解决这个问题，假设您知道如何恢复会话。

def svd(train, test):
samples_per_batch = len(train) // BATCH_SIZE
iter_train = dataio.ShuffleIterator([train["user"],
train["item"],
train["rate"]],
batch_size=BATCH_SIZE)
iter_test = dataio.OneEpochIterator([test["user"],
test["item"],
test["rate"]],
batch_size=-1)
user_batch = tf.placeholder(tf.int32, shape=[None], name="id_user")
item_batch = tf.placeholder(tf.int32, shape=[None], name="id_item")
rate_batch = tf.placeholder(tf.float32, shape=[None])
infer, regularizer = ops.inference_svd(user_batch, item_batch, user_num=USER_NUM, item_num=ITEM_NUM, dim=DIM,
device=DEVICE)
global_step = tf.contrib.framework.get_or_create_global_step()
_, train_op = ops.optimization(infer, regularizer, rate_batch, learning_rate=0.001, reg=0.05, device=DEVICE)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
summary_writer = tf.summary.FileWriter(logdir="/tmp/svd/log", graph=sess.graph)
print("{} {} {} {}".format("epoch", "train_error", "val_error", "elapsed_time"))
errors = deque(maxlen=samples_per_batch)
start = time.time()
for i in range(EPOCH_MAX * samples_per_batch):
users, items, rates = next(iter_train)
_, pred_batch = sess.run([train_op, infer], feed_dict={user_batch: users, item_batch: items, rate_batch: rates})
pred_batch = clip(pred_batch)
errors.append(np.power(pred_batch - rates, 2))
if i % samples_per_batch == 0:
train_err = np.sqrt(np.mean(errors))
test_err2 = np.array([])
for users, items, rates in iter_test:
pred_batch = sess.run(infer, feed_dict={user_batch: users,item_batch: items})
pred_batch = clip(pred_batch)
test_err2 = np.append(test_err2, np.power(pred_batch - rates, 2))
end = time.time()
test_err = np.sqrt(np.mean(test_err2))
print("{:3d} {:f} {:f} {:f}(s)".format(i // samples_per_batch, train_err, test_err, end - start))
train_err_summary = make_scalar_summary("training_error", train_err)
test_err_summary = make_scalar_summary("test_error", test_err)
summary_writer.add_summary(train_err_summary, i)
summary_writer.add_summary(test_err_summary, i)
start = end
# Get the top rated movie for user #1 for every item in the set
userNumber = 1
user_prediction = sess.run(infer, feed_dict={user_batch: np.array([userNumber]), item_batch: np.array(range(ITEM_NUM))})
# The index number is the same as the item number. Orders from lowest (least recommended)
# to largeset
index_rating_order = np.argsort(user_prediction)
print "Top ten recommended items for user {} are".format(userNumber)
print index_rating_order[-10:][::-1]  # at the end, reverse the list
# If you want to include the score:
items_to_choose = index_rating_order[-10:][::-1]
for item, score in zip(items_to_choose, user_prediction[items_to_choose]):
print "{}:  {}".format(item,score)

我所做的唯一更改从第一行注释开始。再次强调，最佳实践是在这个功能中进行训练，但实际上要单独进行预测。