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# look-alike recall
以下是本例的简要目录结构及说明:
```
├── config.yaml # 配置文件
├── data # 样例数据文件夹
│ ├── build_dataset.py # 生成样例数据程序示例
│ └── train_data # 样例数据
│ └── paddle_train.txt # 默认样例数据
├── __init__.py
├── model.py # 模型文件
└── README.md # 文档
```
注:在阅读该示例前,建议您先了解以下内容:
[paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md)
---
## 内容
- [模型简介](#模型简介)
- [数据准备](#数据准备)
- [运行环境](#运行环境)
- [快速开始](#快速开始)
- [论文复现](#论文复现)
- [进阶使用](#进阶使用)
- [FAQ](#FAQ)
## 模型简介
本目录目录模型文件参考论文 [《Real-time Attention Based Look-alike Model for Recommender System》]( https://arxiv.org/pdf/1906.05022),是发表在KDD 19上的一篇论文,文章指出目前基于深度学习的模型没有能够缓解"马太效应",模型倾向于偏向拥有比较多的特征的头部资源,而那些同样优质的缺少用户交互信息的长尾资源得不到充分的曝光。文章提出推荐广告的经典算法 look-alike 是缓解"马太效应"一个不错的选择。但是受限于推荐领域有别于推荐广告严格的实时性要求,该算法未被大规模采用。基于以上,文章提出了一种实时性的基于attention的looka-like算法 RALM。
本项目在paddlepaddle上主要实现RALM的网络结构,其他更多实时性的工程尝试请参考原论文。因为原论文没有在开源数据集上验证模型效果,本项目提供了100行样例数据。验证模型的正确性,若进行精度验证,请参考样例数据格式或者自定义更改相关配置构建自己的数据集,在工程环境中进行验证。
模型大体结构为双塔结构,可以理解为target user 和 user seeds两个塔。使用论文中提出的local_attention 和 global_attention模块。损失函数采用cosine similarity损失函数。
本项目支持功能
训练:单机CPU、单机单卡GPU、单机多卡GPU、本地模拟参数服务器训练、增量训练,配置请参考 [启动训练](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/train.md)
预测:单机CPU、单机单卡GPU ;配置请参考[PaddleRec 离线预测](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/predict.md)
## 数据准备
数据地址:[样例数据](./data/train_data/paddle_train.txt)
样例数据中每条样本包含三个slot:user_seeds, target_user, label。
(1) user_seeds: 基于当前的资源圈定的种子用户
(2) target_user: 目标用户
(3) label: 点击与否
注:本项目提供的样例数据为完全fake的,没有任何实际参考价值。用户可根据样例数据格式自行构建基于自己项目或者工程的数据集。
执行build_dataset.py生成训练集和测试集
```
cd data
python build_dataset.py
```
运行后生成的数据格式为3个离散化特征,用'\t'切分, 对应的slot是user_seeds, target_user, label
```
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
```
## 运行环境
PaddlePaddle>=1.7.2
python 2.7/3.5/3.6/3.7
PaddleRec >=0.1
os : windows/linux/macos
## 快速开始
### 单机训练
CPU环境
在config.yaml文件中设置好设备,epochs等。
```
# select runner by name
mode: [single_cpu_train, single_cpu_infer]
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: single_cpu_train
class: train
# num of epochs
epochs: 4
# device to run training or infer
device: cpu
save_checkpoint_interval: 2 # save model interval of epochs
save_inference_interval: 4 # save inference
save_checkpoint_path: "increment_model" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
print_interval: 10
phases: [phase1]
```
### 单机预测
CPU环境
在config.yaml文件中设置好epochs、device等参数。
```
- name: single_cpu_infer
class: infer
# num of epochs
epochs: 1
# device to run training or infer
device: cpu #选择预测的设备
init_model_path: "increment_dnn" # load model path
phases: [phase2]
```
### 运行
```
python -m paddlerec.run -m paddlerec.models.recall.look-alike_recall
```
### 结果展示
样例数据训练结果展示:
```
PaddleRec: Runner train_runner Begin
Executor Mode: train
processor_register begin
Running SingleInstance.
Running SingleNetwork.
Running SingleStartup.
Running SingleRunner.
I0729 15:51:44.029929 22883 parallel_executor.cc:440] The Program will be executed on CPU using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel.
I0729 15:51:44.031812 22883 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1
I0729 15:51:44.033733 22883 parallel_executor.cc:307] Inplace strategy is enabled, when build_strategy.enable_inplace = True
I0729 15:51:44.035027 22883 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0
batch: 1, BATCH_AUC: [0.], AUC: [0.]
batch: 2, BATCH_AUC: [0.], AUC: [0.]
epoch 0 done, use time: 0.0433671474457
PaddleRec Finish
```
## 论文复现
论文中没有提供基于公开数据集的实验结果。
## 进阶使用
## FAQ
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# global settings
debug: false
workspace: "paddlerec.models.recall.look-alike_recall"
dataset:
- name: sample_1
type: DataLoader
batch_size: 32
data_path: "{workspace}/data/train_data"
sparse_slots: "label user_seeds target_user"
- name: infer_sample
type: DataLoader
batch_size: 32
data_path: "{workspace}/data/train_data"
sparse_slots: "label user_seed target_user"
hyper_parameters:
optimizer:
class: SGD
learning_rate: 0.0001
use_DataLoader: True
user_emb_size: 96
user_count: 100000
seeds_num: 20
transformed_size: 96
mode: train_runner
runner:
- name: train_runner
class: train
epochs: 1
device: cpu
init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
class: infer
device: cpu
init_model_path: "increment/0"
print_interval: 1
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: sample_1
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import random
import pickle
def print_to_file(data, fout, slot):
if not isinstance(data, list):
data = [data]
for i in range(len(data)):
fout.write(slot + ":" + str(data[i]))
fout.write(' ')
fake_seed_users = [i for i in range(2, 20)]
target_user = [1]
length = 100
print("make train data")
with open("paddle_train.txt", "w") as fout:
for _ in range(length):
print_to_file(fake_seed_users, fout, "user_seeds")
print_to_file(target_user, fout, "target_user")
print_to_file(1, fout, "label")
fout.write("\n")
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
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user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
user_seeds:2 user_seeds:3 user_seeds:4 user_seeds:5 user_seeds:6 user_seeds:7 user_seeds:8 user_seeds:9 user_seeds:10 user_seeds:11 user_seeds:12 user_seeds:13 user_seeds:14 user_seeds:15 user_seeds:16 user_seeds:17 user_seeds:18 user_seeds:19 target_user:1 label:1
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.user_emb_size = envs.get_global_env(
"hyper_parameters.user_emb_size", 64)
self.user_count = envs.get_global_env("hyper_parameters.user_count",
100000)
self.transformed_size = envs.get_global_env(
"hyper_parameter.transformed_size", 96)
def local_attention_unit(self, user_seeds, target_user):
wl = fluid.layers.create_parameter(
shape=[self.user_emb_size, self.user_emb_size], dtype="float32")
out = fluid.layers.matmul(user_seeds,
wl) # batch_size * max_len * emb_size
out = fluid.layers.matmul(
out, target_user, transpose_y=True) # batch_size * max_len * 1
out = fluid.layers.tanh(out)
out = fluid.layers.softmax(out, axis=-2)
out = user_seeds * out
out = fluid.layers.reduce_sum(out, dim=1) # batch_size * emb_size
return out
def global_attention_unit(self, user_seeds):
wg = fluid.layers.create_parameter(
shape=[self.user_emb_size, self.user_emb_size], dtype="float32")
out = fluid.layers.matmul(user_seeds, wg)
out = fluid.layers.tanh(out)
out = fluid.layers.matmul(out, user_seeds, transpose_y=True)
out = fluid.layers.softmax(out)
out = fluid.layers.matmul(out, user_seeds)
out = fluid.layers.reduce_sum(out, dim=1)
return out
def net(self, inputs, is_infer=False):
init_value_ = 0.1
user_seeds = self._sparse_data_var[1]
target_user = self._sparse_data_var[2]
self.label = self._sparse_data_var[0]
user_emb_attr = fluid.ParamAttr(name="user_emb")
user_seeds_emb = fluid.embedding(
input=user_seeds,
size=[self.user_count, self.user_emb_size],
param_attr=user_emb_attr,
is_sparse=True)
target_user_emb = fluid.embedding(
input=target_user,
size=[self.user_count, self.user_emb_size],
param_attr=user_emb_attr,
is_sparse=True) # batch_size * 1 * emb_size
user_seeds_emb = fluid.layers.reduce_sum(
user_seeds_emb, dim=1) # batch_size(with lod) * emb_size
pad_value = fluid.layers.assign(input=np.array(
[0.0], dtype=np.float32))
user_seeds_emb, _ = fluid.layers.sequence_pad(
user_seeds_emb, pad_value
) # batch_size(without lod) * max_sequence_length(in batch) * emb_size
target_transform_matrix = fluid.layers.create_parameter(
shape=[self.user_emb_size, self.transformed_size], dtype="float32")
seeds_transform_matrix = fluid.layers.create_parameter(
shape=[self.user_emb_size, self.transformed_size], dtype="float32")
user_seeds_emb_transformed = fluid.layers.matmul(
user_seeds_emb, seeds_transform_matrix)
target_user_emb_transormed = fluid.layers.matmul(
target_user_emb, target_transform_matrix)
seeds_tower = self.local_attention_unit(
user_seeds_emb_transformed,
target_user_emb_transormed) + self.global_attention_unit(
user_seeds_emb_transformed)
target_tower = fluid.layers.reduce_sum(
target_user_emb_transormed, dim=1)
score = fluid.layers.cos_sim(seeds_tower, target_tower)
y_dnn = fluid.layers.cast(self.label, dtype="float32")
self.predict = fluid.layers.sigmoid(score)
cost = fluid.layers.log_loss(
input=score, label=fluid.layers.cast(self.label, "float32"))
avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
label=label_int,
slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
self._infer_results["AUC"] = auc_var
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