音乐推荐系统
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)])
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