LFM介绍
LFM(Funk SVD) 是利用 矩阵分解的推荐算法：
代码语言：javascript复制R = P * Q其中：
- 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
见下图：
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
下载ml-100k 数据集
!wget http://files.grouplens.org/datasets/movielens/ml-100k.zip
!unzip ml-100k.zipResolving 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]
/bin/bash: uzip: command not found评分数据在u.data里，分别是 user_id, movie_id, rating, timestamp
代码语言：javascript复制!head ml-100k/u.data186 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读取数据
代码语言：javascript复制import os
def read_data(path: str, separator: str):
data = []
with open(path, 'r') as f:
for line in f.readlines():
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
data = read_data('ml-100k/u.data', 't')
print(data[0])(0, 0, 0.6)拆分训练集和测试集，test_ratio比例为0.3：
代码语言：javascript复制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
代码语言：javascript复制#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)查看数据
代码语言：javascript复制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准备数据集
代码语言：javascript复制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
代码语言：javascript复制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)编译模型
代码语言：javascript复制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
__________________________________________________________________________________________________下面开始训练10次epochs
代码语言：javascript复制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作者：Jadepeng 出处：jqpeng的技术记事本--http://www.cnblogs.com/xiaoqi 您的支持是对博主最大的鼓励，感谢您的认真阅读。 本文版权归作者所有，欢迎转载，但未经作者同意必须保留此段声明，且在文章页面明显位置给出原文连接，否则保留追究法律责任的权利。
