# 推荐算法之： LFM 推荐算法

## LFM介绍

LFM(Funk SVD) 是利用 矩阵分解的推荐算法：

R  = P * Q


• P矩阵是User-LF矩阵，即用户和隐含特征矩阵
• Q矩阵是LF-Item矩阵，即隐含特征和物品的矩阵
• R：R矩阵是User-Item矩阵，由P*Q得来

R评分举证由于物品和用户数量巨大，且稀疏，因此利用矩阵乘法，转换为 P（n_user * dim) 和 Q （dim*n_count) 两个矩阵，dim 是隐含特征数量。

## Tensorflow实现

!wget http://files.grouplens.org/datasets/movielens/ml-100k.zip
!unzip ml-100k.zip

Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152
Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4924029 (4.7M) [application/zip]
Saving to: ‘ml-100k.zip’

ml-100k.zip         100%[===================>]   4.70M  16.2MB/s    in 0.3s

2020-10-12 12:25:07 (16.2 MB/s) - ‘ml-100k.zip’ saved [4924029/4924029]



!head ml-100k/u.data

186	302	3	891717742
22	377	1	878887116
244	51	2	880606923
166	346	1	886397596
298	474	4	884182806
115	265	2	881171488
253	465	5	891628467
305	451	3	886324817
6	86	3	883603013


import os
data = []
with open(path, 'r') as f:
values = line.strip().split(separator)
user_id, movie_id, rating, timestamp = int(values[0]), int(values[1]), int(values[2]), int(values[3])
data.append((user_id, movie_id, rating, timestamp))
return data

print(data[0])

(0, 0, 0.6)




data = [(d[0], d[1], d[2]/5.0) for d in data]

# 拆分
test_ratio = 0.3
n_test = int(len(data) * test_ratio)
test_data, train_data = data[:n_test], data[n_test:]



id规整化，从0开始增长

#id 规整
def normalize_id(data):
new_data = []
n_user, n_item = 0, 0
user_id_old2new, item_id_old2new = {}, {}
for user_id_old, item_id_old, label in data:
if user_id_old not in user_id_old2new:
user_id_old2new[user_id_old] = n_user
n_user += 1
if item_id_old not in item_id_old2new:
item_id_old2new[item_id_old] = n_item
n_item += 1
new_data.append((user_id_old2new[user_id_old], item_id_old2new[item_id_old], label))
return new_data, n_user, n_item, user_id_old2new, item_id_old2new

data, n_user, n_item, user_id_old2new, item_id_old2new = normalize_id(data)


print(train_data[0:10])
print(test_data[0])
print('n_user',n_user)
print('n_item',n_item)

(196, 242, 0.6)
n_user 943
n_item 1682


import tensorflow as tf

def xy(data):
user_ids = tf.constant([d[0] for d in data], dtype=tf.int32)
item_ids = tf.constant([d[1] for d in data], dtype=tf.int32)
labels = tf.constant([d[2] for d in data], dtype=tf.keras.backend.floatx())
return {'user_id': user_ids, 'item_id': item_ids}, labels

batch = 128
train_ds = tf.data.Dataset.from_tensor_slices(xy(train_data)).shuffle(len(train_data)).batch(batch)
test_ds = tf.data.Dataset.from_tensor_slices(xy(test_data)).batch(batch)


TF模型

def LFM_model(n_user: int, n_item: int, dim=100, l2=1e-6) -> tf.keras.Model:
l2 = tf.keras.regularizers.l2(l2)
user_id = tf.keras.Input(shape=(), name='user_id', dtype=tf.int32)

user_embedding = tf.keras.layers.Embedding(n_user, dim, embeddings_regularizer=l2)(user_id)
# (None,dim)
item_id = tf.keras.Input(shape=(), name='item_id', dtype=tf.int32)
item_embedding = tf.keras.layers.Embedding(n_item, dim, embeddings_regularizer=l2)(item_id)
# (None,dim)
r = user_embedding * item_embedding
y = tf.reduce_sum(r, axis=1)
y = tf.where(y < 0., 0., y)
y = tf.where(y > 1., 1., y)
y = tf.expand_dims(y, axis=1)
return tf.keras.Model(inputs=[user_id, item_id], outputs=y)

model = LFM_model(n_user + 1, n_item + 1, 64)


model.compile(optimizer="adam", loss=tf.losses.MeanSquaredError(), metrics=['AUC', 'Precision', 'Recall'])
model.summary()


__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
user_id (InputLayer)            [(None,)]            0
__________________________________________________________________________________________________
item_id (InputLayer)            [(None,)]            0
__________________________________________________________________________________________________
embedding_12 (Embedding)        (None, 64)           60416       user_id[0][0]
__________________________________________________________________________________________________
embedding_13 (Embedding)        (None, 64)           107712      item_id[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_6 (TensorFlowOp [(None, 64)]         0           embedding_12[0][0]
embedding_13[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_6 (TensorFlowOp [(None,)]            0           tf_op_layer_Mul_6[0][0]
__________________________________________________________________________________________________
tf_op_layer_Less_6 (TensorFlowO [(None,)]            0           tf_op_layer_Sum_6[0][0]
__________________________________________________________________________________________________
tf_op_layer_SelectV2_12 (Tensor [(None,)]            0           tf_op_layer_Less_6[0][0]
tf_op_layer_Sum_6[0][0]
__________________________________________________________________________________________________
tf_op_layer_Greater_6 (TensorFl [(None,)]            0           tf_op_layer_SelectV2_12[0][0]
__________________________________________________________________________________________________
tf_op_layer_SelectV2_13 (Tensor [(None,)]            0           tf_op_layer_Greater_6[0][0]
tf_op_layer_SelectV2_12[0][0]
__________________________________________________________________________________________________
tf_op_layer_ExpandDims_6 (Tenso [(None, 1)]          0           tf_op_layer_SelectV2_13[0][0]
==================================================================================================
Total params: 168,128
Trainable params: 168,128
Non-trainable params: 0
__________________________________________________________________________________________________


model.fit(train_ds,validation_data=test_ds,epochs=10)

Epoch 1/10

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:432: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
547/547 [==============================] - 2s 4ms/step - loss: 0.4388 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.0594 - val_loss: 0.1439 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.4180
Epoch 2/10
547/547 [==============================] - 2s 4ms/step - loss: 0.0585 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.8171 - val_loss: 0.0486 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8655
Epoch 3/10
547/547 [==============================] - 2s 3ms/step - loss: 0.0393 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.9053 - val_loss: 0.0433 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8982
Epoch 4/10
547/547 [==============================] - 2s 3ms/step - loss: 0.0346 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.9107 - val_loss: 0.0415 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8947
Epoch 5/10
547/547 [==============================] - 2s 4ms/step - loss: 0.0301 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.9071 - val_loss: 0.0410 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8869
Epoch 6/10
547/547 [==============================] - 2s 4ms/step - loss: 0.0257 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.8958 - val_loss: 0.0410 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8849
Epoch 7/10
547/547 [==============================] - 2s 4ms/step - loss: 0.0218 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.8844 - val_loss: 0.0414 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8753
Epoch 8/10
547/547 [==============================] - 2s 4ms/step - loss: 0.0183 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.8719 - val_loss: 0.0425 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8659
Epoch 9/10
547/547 [==============================] - 2s 4ms/step - loss: 0.0153 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.8624 - val_loss: 0.0435 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8620
Epoch 10/10
547/547 [==============================] - 2s 4ms/step - loss: 0.0132 - auc: 0.0000e+00 - precision: 1.0000 - recall: 0.8535 - val_loss: 0.0449 - val_auc: 0.0000e+00 - val_precision: 1.0000 - val_recall: 0.8531


posted @ 2020-10-12 21:10  JadePeng  阅读(802)  评论(0编辑  收藏  举报