笔者最近面试到了网易新闻推荐部门，考了一点推荐系统的知识，算是被虐惨了。于是乎自己怒补了一些知识。记录一点关于推荐系统的知识和实现。
音乐推荐系统，这里的简单指的是数据量级才2万条，之后会详细解释。
1. 推荐系统工程师人才成长RoadMap
2. 1. 数据的获取
任何的机器学习算法解决问题，首先就是要考虑的是数据，数据从何而来？
对于网易云音乐这样的企业而言，用户的收藏和播放数据是可以直接获得的，我们找一个取巧的方式，包含用户音乐兴趣信息，同时又可以获取的数据是什么？
对的，是热门歌单信息，以及歌单内歌曲的详细信息。 
3. 数据爬虫脚本
代码说明：
1. 网易云音乐网络爬虫由于加了数据包传动态参数的反爬措施。拿到歌单数据包的难度很大。一大神破解了传参动态密码，代码中AES算法。
2. 但是不知道为什么这个python2.7版下脚本只能爬取每个歌单里面的10首歌，由于这个原因，导致我们的推荐系统原始数据量级骤然降低。笔者试了很久，也没有办法。望大家给点建议。不管怎样，数据量小，那咱们就简单实现就好。
3. 一共1921个歌单（json文件），每个歌单里面包含10首歌，所以咱们后面建模的数据量实际只有2W左右的实例。
# -*- coding:utf-8 -*-
"""
爬虫爬取网易云音乐歌单的数据包保存成json文件
python2.7环境
"""
import requests
import json
import os
import base64
import binascii
import urllib
import urllib2
from Crypto.Cipher import AES
from bs4 import BeautifulSoup
class NetEaseAPI:
def __init__(self):
self.header = {
'Host': 'music.163.com',
'Origin': 'https://music.163.com',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:56.0) Gecko/20100101 Firefox/56.0',
'Accept': 'application/json, text/javascript',
'Accept-Language': 'zh-CN,zh;q=0.9',
'Connection': 'keep-alive',
'Content-Type': 'application/x-www-form-urlencoded',
}
self.cookies = {'appver': '1.5.2'}
self.playlist_class_dict = {}
self.session = requests.Session()
def _http_request(self, method, action, query=None, urlencoded=None, callback=None, timeout=None):
connection = json.loads(self._raw_http_request(method, action, query, urlencoded, callback, timeout))
return connection
def _raw_http_request(self, method, action, query=None, urlencoded=None, callback=None, timeout=None):
if method == 'GET':
request = urllib2.Request(action, self.header)
response = urllib2.urlopen(request)
connection = response.read()
elif method == 'POST':
data = urllib.urlencode(query)
request = urllib2.Request(action, data, self.header)
response = urllib2.urlopen(request)
connection = response.read()
return connection
@staticmethod
def _aes_encrypt(text, secKey):
pad = 16 - len(text) % 16
text = text + chr(pad) * pad
encryptor = AES.new(secKey, 2, '0102030405060708')
ciphertext = encryptor.encrypt(text)
ciphertext = base64.b64encode(ciphertext).decode('utf-8')
return ciphertext
@staticmethod
def _rsa_encrypt(text, pubKey, modulus):
text = text[::-1]
rs = pow(int(binascii.hexlify(text), 16), int(pubKey, 16), int(modulus, 16))
return format(rs, 'x').zfill(256)
@staticmethod
def _create_secret_key(size):
return (''.join(map(lambda xx: (hex(ord(xx))[2:]), os.urandom(size))))[0:16]
def get_playlist_id(self, action):
request = urllib2.Request(action, headers=self.header)
response = urllib2.urlopen(request)
html = response.read().decode('utf-8')
response.close()
soup = BeautifulSoup(html, 'lxml')
list_url = soup.select('ul#m-pl-container li div a.msk')
for k, v in enumerate(list_url):
list_url[k] = v['href'][13:]
return list_url
def get_playlist_detail(self, id):
text = {
'id': id,
'limit': '100',
'total': 'true'
}
text = json.dumps(text)
nonce = '0CoJUm6Qyw8W8jud'
pubKey = '010001'
modulus = ('00e0b509f6259df8642dbc35662901477df22677ec152b5ff68ace615bb7'
'b725152b3ab17a876aea8a5aa76d2e417629ec4ee341f56135fccf695280'
'104e0312ecbda92557c93870114af6c9d05c4f7f0c3685b7a46bee255932'
'575cce10b424d813cfe4875d3e82047b97ddef52741d546b8e289dc6935b'
'3ece0462db0a22b8e7')
secKey = self._create_secret_key(16)
encText = self._aes_encrypt(self._aes_encrypt(text, nonce), secKey)
encSecKey = self._rsa_encrypt(secKey, pubKey, modulus)
data = {
'params': encText,
'encSecKey': encSecKey
}
action = 'http://music.163.com/weapi/v3/playlist/detail'
playlist_detail = self._http_request('POST', action, data)
return playlist_detail
if __name__ == '__main__':
nn = NetEaseAPI()
index = 1
for flag in range(1, 38):
if flag > 1:
page = (flag - 1) * 35
url = 'http://music.163.com/discover/playlist/?order=hot&cat=%E5%85%A8%E9%83%A8&limit=35&offset=' + str(
page)
else:
url = 'http://music.163.com/discover/playlist'
playlist_id = nn.get_playlist_id(url)
for item_id in playlist_id:
playlist_detail = nn.get_playlist_detail(item_id)
with open('{0}.json'.format(index), 'w') as file_obj:
json.dump(playlist_detail, file_obj, ensure_ascii=False)
index += 1
print("写入json文件：", item_id)4. 特征工程和数据预处理，提取我这次做推荐系统有用的特征信息。
在原始的1291个json文件里面包含非常多的信息(风格，歌手，歌曲播放次数，歌曲时长，歌曲发行时间)，其实大家思考后一定会想到如何使用它们进一步完善推荐系统。我这里依旧使用最基础的音乐信息，我们认为同一个歌单中的歌曲，有比较高的相似性，
其中 歌单数据=>推荐系统格式数据，主流的python推荐系统框架，支持的最基本数据格式为movielens dataset，其评分数据格式为 user item rating timestamp，为了简单，我们也把数据处理成这个格式。
# -*- coding:utf-8-*-
"""
对网易云所有歌单爬虫的json文件进行数据预处理成csv文件
python3.6环境
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import json
def parse_playlist_item():
"""
:return: 解析成userid itemid rating timestamp行格式
"""
file = open("neteasy_playlist_recommend_data.csv", 'a', encoding='utf8')
for i in range(1, 1292):
with open("neteasy_playlist_data/{0}.json".format(i), 'r', encoding='UTF-8') as load_f:
load_dict = json.load(load_f)
try:
for item in load_dict['playlist']['tracks']:
# playlist id # song id # score # datetime
line_result = [load_dict['playlist']['id'], item['id'], item['pop'], item['publishTime']]
for k, v in enumerate(line_result):
if k == len(line_result) - 1:
file.write(str(v))
else:
file.write(str(v) + ',')
file.write('\n')
except Exception:
print(i)
continue
file.close()
def parse_playlist_id_to_name():
file = open("neteasy_playlist_id_to_name_data.csv", 'a', encoding='utf8')
for i in range(1, 1292):
with open("neteasy_playlist_data/{0}.json".format(i), 'r', encoding='UTF-8') as load_f:
load_dict = json.load(load_f)
try:
line_result = [load_dict['playlist']['id'], load_dict['playlist']['name']]
for k, v in enumerate(line_result):
if k == len(line_result) - 1:
file.write(str(v))
else:
file.write(str(v) + ',')
file.write('\n')
except Exception:
print(i)
continue
file.close()
def parse_song_id_to_name():
file = open("neteasy_song_id_to_name_data.csv", 'a', encoding='utf8')
for i in range(1, 1292):
with open("neteasy_playlist_data/{0}.json".format(i), 'r', encoding='UTF-8') as load_f:
load_dict = json.load(load_f)
try:
for item in load_dict['playlist']['tracks']:
# playlist id # song id # score # datetime
line_result = [item['id'], item['name'] + '-' + item['ar'][0]['name']]
for k, v in enumerate(line_result):
if k == len(line_result) - 1:
file.write(str(v))
else:
file.write(str(v) + ',')
file.write('\n')
except Exception:
print(i)
continue
file.close()
# parse_playlist_item()
# parse_playlist_id_to_name()
# parse_song_id_to_name()5. 数据说明
我们需要保存 歌单id=>歌单名 和 歌曲id=>歌曲名 的信息后期备用。
歌曲id=>歌曲名： 
歌单id=>歌单名： 
6. 推荐系统常见的工程化做法
project = offline modelling + online prediction
1）offline
python脚本语言
2）online
效率至上 C++/Java
原则：能离线预先算好的，都离线算好，最优的形式：线上是一个K-V字典
1.针对用户推荐 网易云音乐(每日30首歌/7首歌)
2.针对歌曲 在你听某首歌的时候，找“相似歌曲”
7. Surprise推荐库简单介绍
在推荐系统的建模过程中，我们将用到python库 Surprise(Simple Python RecommendatIon System Engine)，是scikit系列中的一个(很多同学用过scikit-learn和scikit-image等库)。
具体的配合这篇博文（Python推荐系统库——Surprise）深入学习Surprise。
8. 网易云音乐歌单推荐
利用surprise推荐库中KNN协同过滤算法进行已有数据的建模，并且推荐相似的歌单预测
# -*- coding:utf-8-*-
"""
利用surprise推荐库 KNN协同过滤算法推荐网易云歌单
python2.7环境
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import os
import csv
from surprise import KNNBaseline, Reader, KNNBasic, KNNWithMeans,evaluate
from surprise import Dataset
def recommend_model():
file_path = os.path.expanduser('neteasy_playlist_recommend_data.csv')
# 指定文件格式
reader = Reader(line_format='user item rating timestamp', sep=',')
# 从文件读取数据
music_data = Dataset.load_from_file(file_path, reader=reader)
# 计算歌曲和歌曲之间的相似度
train_set = music_data.build_full_trainset()
print('开始使用协同过滤算法训练推荐模型...')
algo = KNNBasic()
algo.fit(train_set)
return algo
def playlist_data_preprocessing():
csv_reader = csv.reader(open('neteasy_playlist_id_to_name_data.csv'))
id_name_dic = {}
name_id_dic = {}
for row in csv_reader:
id_name_dic[row[0]] = row[1]
name_id_dic[row[1]] = row[0]
return id_name_dic, name_id_dic
def song_data_preprocessing():
csv_reader = csv.reader(open('neteasy_song_id_to_name_data.csv'))
id_name_dic = {}
name_id_dic = {}
for row in csv_reader:
id_name_dic[row[0]] = row[1]
name_id_dic[row[1]] = row[0]
return id_name_dic, name_id_dic
def playlist_recommend_main():
print("加载歌单id到歌单名的字典映射...")
print("加载歌单名到歌单id的字典映射...")
id_name_dic, name_id_dic = playlist_data_preprocessing()
print("字典映射成功...")
print('构建数据集...')
algo = recommend_model()
print('模型训练结束...')
current_playlist_id = id_name_dic.keys()[200]
print('当前的歌单id：' + current_playlist_id)
current_playlist_name = id_name_dic[current_playlist_id]
print('当前的歌单名字：' + current_playlist_name)
playlist_inner_id = algo.trainset.to_inner_uid(current_playlist_id)
print('当前的歌单内部id：' + str(playlist_inner_id))
playlist_neighbors = algo.get_neighbors(playlist_inner_id, k=10)
playlist_neighbors_id = (algo.trainset.to_raw_uid(inner_id) for inner_id in playlist_neighbors)
# 把歌曲id转成歌曲名字
playlist_neighbors_name = (id_name_dic[playlist_id] for playlist_id in playlist_neighbors_id)
print("和歌单<", current_playlist_name, '> 最接近的10个歌单为：\n')
for playlist_name in playlist_neighbors_name:
print(playlist_name, name_id_dic[playlist_name])
playlist_recommend_main()
# "E:\ProgramingSoftware\PyCharm Community Edition 2016.2.3\Anaconda2\python2.exe" C:/Users/Administrator/Desktop/博客素材/recommend_system_learning/recommend_main.py
# 加载歌单id到歌单名的字典映射...
# 加载歌单名到歌单id的字典映射...
# 字典映射成功...
# 构建数据集...
# 开始使用协同过滤算法训练推荐模型...
# Computing the msd similarity matrix...
# Done computing similarity matrix.
# 模型训练结束...
# 当前的歌单id：2056644233
# 当前的歌单名字：暖阳微醺◎来碗甜度100%的糖水吧
# 当前的歌单内部id：444
# 和歌单< 暖阳微醺◎来碗甜度100%的糖水吧 > 最接近的10个歌单为：
#
# 2018全年抖腿指南，老铁你怕了吗？ 2050704516
# 2018欧美最新流行单曲推荐【持续更新】 2042762698
# 「女毒电子」●酒心巧克力般的甜蜜圈套 2023282769
# 『 2018优质新歌电音推送 』 2000367772
# 那些为电音画龙点睛的惊艳女Vocals 2081768956
# 女嗓篇 |不可以这么俏皮清新 我会喜欢你的 2098623867
# 「柔美唱腔」时光不敌粉嫩少女心 2093450772
# 「节奏甜食」次点甜醹发酵的牛奶草莓 2069080336
# 03.23 ✘ 欧美热浪新歌 ‖ 周更向 2151684623
# 开门呀 小可爱送温暖 2151816466
#
# Process finished with exit code 0协同过滤模型的评估验证方法：
file_path = os.path.expanduser('neteasy_playlist_recommend_data.csv')
# 指定文件格式
reader = Reader(line_format='user item rating timestamp', sep=',')
# 从文件读取数据
music_data = Dataset.load_from_file(file_path, reader=reader)
# 分成5折
music_data.split(n_folds=5)
algo = KNNBasic()
perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'])
print(perf)
"""
Evaluating RMSE, MAE of algorithm KNNBasic.
------------
Fold 1
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 85.4426
MAE: 82.4766
------------
Fold 2
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 85.2970
MAE: 82.0756
------------
Fold 3
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 85.2267
MAE: 82.0697
------------
Fold 4
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 85.3390
MAE: 82.1538
------------
Fold 5
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 86.0862
MAE: 83.2907
------------
------------
Mean RMSE: 85.4783
Mean MAE : 82.4133
------------
------------
defaultdict(<type 'list'>, {u'mae': [82.476559473072456, 82.075552111584656, 82.069740410693527, 82.153816350251844, 83.29069767441861], u'rmse': [85.442585928330303, 85.29704915378538, 85.22667089592963, 85.339041675515148, 86.086152088447705]})
"""源码全部都在Github上：recommend_system_learning
来源：CSDN
作者：Tong_T
链接：https://blog.csdn.net/Tong_T/article/details/80354512