音乐推荐系统（一）

数据获取

任何的机器学习算法解决问题，首先要考虑的是数据，数据从何而来？
对于酷狗音乐/网易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----歌单

数据说明

歌单的形式

{
    "result": {
        "id": 111450065,
        "status": 0,
        "commentThreadId": "A_PL_0_111450065",
        "trackCount": 120,
        "updateTime": 1460164523907,
        "commentCount": 227,
        "ordered": true,
        "anonimous": false,
        "highQuality": false,
        "subscribers": [],
        "playCount": 687070,
        "trackNumberUpdateTime": 1460164523907,
        "createTime": 1443528317662,
        "name": "带本书去旅行吧,人生最美好的时光在路上。",
        "cloudTrackCount": 0,
        "shareCount": 149,
        "adType": 0,
        "trackUpdateTime": 1494134249465,
        "userId": 39256799,
        "coverImgId": 3359008023885470,
        "coverImgUrl": "http://p1.music.126.net/2ZFcuSJ6STR8WgzkIi2U-Q==/3359008023885470.jpg",
        "artists": null,
        "newImported": false,
        "subscribed": false,
        "privacy": 0,
        "specialType": 0,
        "description": "现在是一年中最美好的时节，世界上很多地方都不冷不热，有湛蓝的天空和清冽的空气，正是出游的好时光。长假将至，你是不是已经收拾行装准备出发了？行前焦虑症中把衣服、洗漱用品、充电器之类东西忙忙碌碌地丢进箱子，打进背包的时候，我打赌你肯定会留个位置给一位好朋友：书。不是吗？不管是打发时间，小读怡情，还是为了做好攻略备不时之需，亦或是为了小小地装上一把，你都得有一本书傍身呀。读大仲马，我是复仇的伯爵；读柯南道尔，我穿梭在雾都的暗夜；读村上春树，我是寻羊的冒险者；读马尔克斯，目睹百年家族兴衰；读三毛，让灵魂在撒哈拉流浪；读老舍，嗅着老北京的气息；读海茵莱茵，于科幻狂流遨游；读卡夫卡，在城堡中审判……读书的孩子不会孤单，读书的孩子永远幸福。",
        "subscribedCount": 10882,
        "totalDuration": 0,
        "tags": [
            "旅行",
            "钢琴",
            "安静"]
        "creator": {
            "followed": false,
            "remarkName": null,
            "expertTags": [
                "古典",
                "民谣",
                "华语"
            ],
            "userId": 39256799,
            "authority": 0,
            "userType": 0,
            "gender": 1,
            "backgroundImgId": 3427177752524551,
            "city": 360600,
            "mutual": false,
            "avatarUrl": "http://p1.music.126.net/TLRTrJpOM5lr68qJv1IyGQ==/1400777825738419.jpg",
            "avatarImgIdStr": "1400777825738419",
            "detailDescription": "",
            "province": 360000,
            "description": "",
            "birthday": 637516800000,
            "nickname": "有梦人生不觉寒",
            "vipType": 0,
            "avatarImgId": 1400777825738419,
            "defaultAvatar": false,
            "djStatus": 0,
            "accountStatus": 0,
            "backgroundImgIdStr": "3427177752524551",
            "backgroundUrl": "http://p1.mus## 标题ic.126.net/LS96S_6VP9Hm7-T447-X0g==/3427177752524551.jpg",
            "signature": "漫无目的的乱听，听着，听着，竟然灵魂出窍了。更多精品音乐美图分享请加我微信hu272367751。微信是我的精神家园，有我最真诚的分享。",
            "authStatus": 0}
        "tracks": [{歌曲1},{歌曲2}, ...]
     }
}

歌曲的格式

{
    "id": 29738501,
    "name": "跟着你到天边 钢琴版",
    "duration": 174001,
    "hearTime": 0,
    "commentThreadId": "R_SO_4_29738501",
    "score": 40,
    "mvid": 0,
    "hMusic": null,
    "disc": "",
    "fee": 0,
    "no": 1,
    "rtUrl": null,
    "ringtone": null,
    "rtUrls": [],
    "rurl": null,
    "status": 0,
    "ftype": 0,
    "mp3Url": "http://m2.music.126.net/vrVa20wHs8iIe0G8Oe7I9Q==/3222668581877701.mp3",
    "audition": null,
    "playedNum": 0,
    "copyrightId": 0,
    "rtype": 0,
    "crbt": null,
    "popularity": 40,
    "dayPlays": 0,
    "alias": [],
    "copyFrom": "",
    "position": 1,
    "starred": false,,
    "starredNum": 0
    "bMusic": {
        "name": "跟着你到天边 钢琴版",
        "extension": "mp3",
        "volumeDelta": 0.0553125,
        "sr": 44100,
        "dfsId": 3222668581877701,
        "playTime": 174001,
        "bitrate": 96000,
        "id": 52423394,
        "size": 2089713
    },
    "lMusic": {
        "name": "跟着你到天边 钢琴版",
        "extension": "mp3",
        "volumeDelta": 0.0553125,
        "sr": 44100,
        "dfsId": 3222668581877701,
        "playTime": 174001,
        "bitrate": 96000,
        "id": 52423394,
        "size": 2089713
    },
    "mMusic": {
        "name": "跟着你到天边 钢琴版",
        "extension": "mp3",
        "volumeDelta": -0.000265076,
        "sr": 44100,
        "dfsId": 3222668581877702,
        "playTime": 174001,
        "bitrate": 128000,
        "id": 52423395,
        "size": 2785510
    },
    "artists": [
        {
        "img1v1Url": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg",
        "name": "群星",
        "briefDesc": "",
        "albumSize": 0,
        "img1v1Id": 0,
        "musicSize": 0,
        "alias": [],
        "picId": 0,
        "picUrl": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg",
        "trans": "",
        "id": 122455
        }
    ],
    "album": {
        "id": 3054006,
        "status": 2,
        "type": null,
        "tags": "",
        "size": 69,
        "blurPicUrl": "http://p1.music.126.net/2XLMVZhzVZCOunaRCOQ7Bg==/3274345629219531.jpg",
        "copyrightId": 0,
        "name": "热门华语248",
        "companyId": 0,
        "songs": [],
        "description": "",
        "pic": 3274345629219531,
        "commentThreadId": "R_AL_3_3054006",
        "publishTime": 1388505600004,
        "briefDesc": "",
        "company": "",
        "picId": 3274345629219531,
        "alias": [],
        "picUrl": "http://p1.music.126.net/2XLMVZhzVZCOunaRCOQ7Bg==/3274345629219531.jpg",
        "artists": [
        {
            "img1v1Url": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg",
            "name": "群星",
            "briefDesc": "",
            "albumSize": 0,
            "img1v1Id": 0,
            "musicSize": 0,
            "alias": [],
            "picId": 0,
            "picUrl": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg",
            "trans": "",
            "id": 122455
        }
        ],
        "artist": {
        "img1v1Url": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg",
        "name": "",
        "briefDesc": "",
        "albumSize": 0,
        "img1v1Id": 0,
        "musicSize": 0,
        "alias": [],
        "picId": 0,
        "picUrl": "http://p1.music.126.net/6y-UleORITEDbvrOLV0Q8A==/5639395138885805.jpg",
        "trans": "",
        "id": 0
        }
    }
}

数据解析

给大家原始数据和这份数据说明的原因是：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…)，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
我们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，同时都是做单的同学喜欢的

原始数据=>歌单数据

抽取 歌单名称，歌单id，收藏数，所属分类 4个歌单维度的信息
抽取 歌曲id，歌曲名，歌手，歌曲热度 等4个维度信息歌曲的信息

组织成如下格式：

漫步西欧小镇上##小语种,旅行##69413685##474 18682332::Wäg vo dir::Joy Amelie::70.0 4335372::Only When I Sleep::The Corrs::60.0 2925502::Si Seulement::Lynnsha::100.0 21014930::Tu N’As Pas Cherché…::La Grande Sophie::100.0 20932638::Du behöver aldrig mer vara rädd::Lasse Lindh::25.0 17100518::Silent Machine::Cat Power::60.0 3308096::Kor pai kon diew : ชอไปคนเดียว::Palmy::5.0 1648250::les choristes::Petits Chanteurs De Saint Marc::100.0 4376212::Paddy’s Green Shamrock Shore::The High Kings::25.0 2925400::A Todo Color::Las Escarlatinas::95.0 19711402::Comme Toi::Vox Angeli::75.0 3977526::Stay::Blue Cafe::100.0 2538518::Shake::Elize::85.0 2866799::Mon Ange::Jena Lee::85.0 5191949::Je M’appelle Helene::Hélène Rolles::85.0 20036323::Ich Lieb’ Dich Immer Noch So Sehr::Kate & Ben::100.0

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。
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

保存歌单和歌曲信息备用

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。

#coding: utf-8
import cPickle as pickle
import sys

def parse_playlist_get_info(in_line, playlist_dic, song_dic):
	contents = in_line.strip().split("\t")
	name, tags, playlist_id, subscribed_count = contents[0].split("##")
	playlist_dic[playlist_id] = name
	for song in contents[1:]:
		try:
			song_id, song_name, artist, popularity = song.split(":::")
			song_dic[song_id] = song_name+"\t"+artist
		except:
			print "song format error"
			print song+"\n"

		

def parse_file(in_file, out_playlist, out_song):
	#从歌单id到歌单名称的映射字典
	playlist_dic = {}
	#从歌曲id到歌曲名称的映射字典
	song_dic = {}
	for line in open(in_file):
		parse_playlist_get_info(line, playlist_dic, song_dic)
	#把映射字典保存在二进制文件中
	pickle.dump(playlist_dic, open(out_playlist,"wb")) 
	#可以通过 playlist_dic = pickle.load(open("playlist.pkl","rb"))重新载入
	pickle.dump(song_dic, open(out_song,"wb"))

python推荐系统库Surprise

1.简单易用，同时支持多种推荐算法：
基础算法/baseline algorithms
基于近邻方法(协同过滤)/neighborhood methods
矩阵分解方法/matrix factorization-based (SVD, PMF, SVD++, NMF)
2.其中基于近邻的方法(协同过滤)可以设定不同的度量准则

3.支持不同的评估准则

使用示例

基本方法

# 可以使用上面提到的各种推荐系统算法
from surprise import SVD
from surprise import Dataset
from surprise import evaluate, print_perf

# 默认载入movielens数据集
data = Dataset.load_builtin('ml-100k')
# k折交叉验证(k=3)
data.split(n_folds=3)
# 试一把SVD矩阵分解
algo = SVD()
# 在数据集上测试一下效果
perf = evaluate(algo, data, measures=['RMSE', 'MAE'])
#输出结果
print_perf(perf)

载入自己的数据集方法

# 指定文件所在路径
file_path = os.path.expanduser('~/.surprise_data/ml-100k/ml-100k/u.data')
# 告诉文本阅读器，文本的格式是怎么样的
reader = Reader(line_format='user item rating timestamp', sep='\t')
# 加载数据
data = Dataset.load_from_file(file_path, reader=reader)
# 手动切分成5折(方便交叉验证)
data.split(n_folds=5)

算法调参(让推荐系统有更好的效果)

这里实现的算法用到的算法无外乎也是SGD等，因此也有一些超参数会影响最后的结果，我们同样可以用sklearn中常用到的网格搜索交叉验证(GridSearchCV)来选择最优的参数。简单的例子如下所示：

# 定义好需要优选的参数网格
param_grid = {'n_epochs': [5, 10], 'lr_all': [0.002, 0.005],
              'reg_all': [0.4, 0.6]}
# 使用网格搜索交叉验证
grid_search = GridSearch(SVD, param_grid, measures=['RMSE', 'FCP'])
# 在数据集上找到最好的参数
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)
grid_search.evaluate(data)
# 输出调优的参数组 
# 输出最好的RMSE结果
print(grid_search.best_score['RMSE'])
# >>> 0.96117566386

# 输出对应最好的RMSE结果的参数
print(grid_search.best_params['RMSE'])
# >>> {'reg_all': 0.4, 'lr_all': 0.005, 'n_epochs': 10}

# 最好的FCP得分
print(grid_search.best_score['FCP'])
# >>> 0.702279736531

# 对应最高FCP得分的参数
print(grid_search.best_params['FCP'])
# >>> {'reg_all': 0.6, 'lr_all': 0.005, 'n_epochs': 10}

数据集上训练模型

用协同过滤构建模型并进行预测

movielens的例子

# 可以使用上面提到的各种推荐系统算法
from surprise import KNNWithMeans
from surprise import Dataset
from surprise import evaluate, print_perf

# 默认载入movielens数据集
data = Dataset.load_builtin('ml-100k')
# k折交叉验证(k=3)
data.split(n_folds=3)
# 试一把SVD矩阵分解
algo = KNNWithMeans()
# 在数据集上测试一下效果
perf = evaluate(algo, data, measures=['RMSE', 'MAE'])
#输出结果
print_perf(perf)

"""
以下的程序段告诉大家如何在协同过滤算法建模以后，根据一个item取回相似度最高的item，主要是用到algo.get_neighbors()这个函数
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
import os
import io

from surprise import KNNBaseline
from surprise import Dataset


def read_item_names():
    """
    获取电影名到电影id 和 电影id到电影名的映射
    """

    file_name = (os.path.expanduser('~') +
                 '/.surprise_data/ml-100k/ml-100k/u.item')
    rid_to_name = {}
    name_to_rid = {}
    with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
        for line in f:
            line = line.split('|')
            rid_to_name[line[0]] = line[1]#id 到name
            name_to_rid[line[1]] = line[0]

    return rid_to_name, name_to_rid


# 首先，用算法计算相互间的相似度
data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()#构建稀疏矩阵
sim_options = {'name': 'pearson_baseline', 'user_based': False}
algo = KNNBaseline(sim_options=sim_options)
algo.train(trainset)
# 获取电影名到电影id 和 电影id到电影名的映射
rid_to_name, name_to_rid = read_item_names()
# 拿出来Toy Story这部电影对应的item id
toy_story_raw_id = name_to_rid['Toy Story (1995)']
toy_story_raw_id
 #rid 原始编号 iid 内部映射ID
toy_story_inner_id = algo.trainset.to_inner_iid(toy_story_raw_id)
toy_story_inner_id
# 找到最近的10个邻居
toy_story_neighbors = algo.get_neighbors(toy_story_inner_id, k=10)
toy_story_neighbors
# 从近邻的id映射回电影名称  rid 原始编号 iid 内部映射ID
toy_story_neighbors = (algo.trainset.to_raw_iid(inner_id)
                       for inner_id in toy_story_neighbors)
toy_story_neighbors = (rid_to_name[rid]
                       for rid in toy_story_neighbors)

print()
print('The 10 nearest neighbors of Toy Story are:')
for movie in toy_story_neighbors:
    print(movie)
# 拿出来Toy Story这部电影对应的item id
toy_story_raw_id = name_to_rid['Toy Story (1995)']
toy_story_inner_id = algo.trainset.to_inner_iid(toy_story_raw_id)

# 找到最近的10个邻居
toy_story_neighbors = algo.get_neighbors(toy_story_inner_id, k=10)

# 从近邻的id映射回电影名称
toy_story_neighbors = (algo.trainset.to_raw_iid(inner_id)
                       for inner_id in toy_story_neighbors)
toy_story_neighbors = (rid_to_name[rid]
                       for rid in toy_story_neighbors)

print()
print('The 10 nearest neighbors of Toy Story are:')
for movie in toy_story_neighbors:
    print(movie)

音乐预测的例子

from __future__ import (absolute_import, division, print_function, unicode_literals)
import os
import io

from surprise import KNNBaseline, Reader
from surprise import Dataset

import cPickle as pickle
# 重建歌单id到歌单名的映射字典
id_name_dic = pickle.load(open("popular_playlist.pkl","rb"))
print("加载歌单id到歌单名的映射字典完成...")
# 重建歌单名到歌单id的映射字典
name_id_dic = {}
for playlist_id in id_name_dic:
    name_id_dic[id_name_dic[playlist_id]] = playlist_id
print("加载歌单名到歌单id的映射字典完成...")


file_path = os.path.expanduser('./popular_music_suprise_format.txt')
# 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
print("构建数据集...")
trainset = music_data.build_full_trainset()
#sim_options = {'name': 'pearson_baseline', 'user_based': False}

查找最近的user(在这里是歌单)

print("开始训练模型...")
#sim_options = {'user_based': False}
#algo = KNNBaseline(sim_options=sim_options)
algo = KNNBaseline()
algo.train(trainset)

current_playlist = name_id_dic.keys()[39]
print("歌单名称", current_playlist)

# 取出近邻
# 映射名字到id
playlist_id = name_id_dic[current_playlist]
print("歌单id", playlist_id)
# 取出来对应的内部user id => to_inner_uid
playlist_inner_id = algo.trainset.to_inner_uid(playlist_id)
print("内部id", playlist_inner_id)

playlist_neighbors = algo.get_neighbors(playlist_inner_id, k=10)

# 把歌曲id转成歌曲名字
# to_raw_uid映射回去
playlist_neighbors = (algo.trainset.to_raw_uid(inner_id)
                       for inner_id in playlist_neighbors)
playlist_neighbors = (id_name_dic[playlist_id]
                       for playlist_id in playlist_neighbors)

print()
print("和歌单 《", current_playlist, "》 最接近的10个歌单为：\n")
for playlist in playlist_neighbors:
    print(playlist, algo.trainset.to_inner_uid(name_id_dic[playlist]))

针对用户进行预测

import cPickle as pickle
# 重建歌曲id到歌曲名的映射字典
song_id_name_dic = pickle.load(open("popular_song.pkl","rb"))
print("加载歌曲id到歌曲名的映射字典完成...")
# 重建歌曲名到歌曲id的映射字典
song_name_id_dic = {}
for song_id in song_id_name_dic:
    song_name_id_dic[song_id_name_dic[song_id]] = song_id
print("加载歌曲名到歌曲id的映射字典完成...")
#内部编码的4号用户
user_inner_id = 4
user_rating = trainset.ur[user_inner_id]
items = map(lambda x:x[0], user_rating)
for song in items:
    print(algo.predict(user_inner_id, song, r_ui=1), song_id_name_dic[algo.trainset.to_raw_iid(song)])

用矩阵分解进行预测

### 使用NMF
from surprise import NMF, evaluate
from surprise import Dataset

file_path = os.path.expanduser('./popular_music_suprise_format.txt')
# 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)
# 构建数据集和建模
algo = NMF()
trainset = music_data.build_full_trainset()
algo.train(trainset)
user_inner_id = 4
user_rating = trainset.ur[user_inner_id]
items = map(lambda x:x[0], user_rating)
for song in items:
    print(algo.predict(algo.trainset.to_raw_uid(user_inner_id), algo.trainset.to_raw_iid(song), r_ui=1), song_id_name_dic[algo.trainset.to_raw_iid(song)])