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最新推荐文章于 2024-09-09 10:42:44 发布

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最新推荐文章于 2024-09-09 10:42:44 发布

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本文介绍了一种使用Python的gensim库实现歌单序列的Song2Vec模型训练过程。该模型通过解析包含歌曲ID及名称等信息的歌单数据，利用Word2Vec算法进行训练，最终将歌曲表示为向量形式，便于后续推荐系统的构建。

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#coding: utf-8
import multiprocessing
import gensim
import sys

def parse_playlist_get_sequence(in_line, playlist_sequence):
song_sequence = []
contents = in_line.strip().split("\t")
# 解析歌单序列
for song in contents[1:]:
  try:
   song_id, song_name, artist, popularity = song.split(":::")
   song_sequence.append(song_id)
  except:
   print "song format error"
   print song+"\n"
playlist_sequence.append(song_sequence)

def train_song2vec(in_file, out_file):
#所有歌单序列
playlist_sequence = []
#遍历所有歌单
for line in open(in_file):
parse_playlist_get_sequence(line, playlist_sequence)
#使用word2vec训练
cores = multiprocessing.cpu_count()
print "using all "+str(cores)+" cores"
print "Training word2vec model..."
model = gensim.models.Word2Vec(sentences=playlist_sequence, size=150, min_count=1, window=3, workers=cores)
print "Saving model..."
model.save(out_file)

def test_song2vec_model(model, song_id):

if __name__ == '__main__':
in_file = sys.argv[1]
out_file = sys.argv[2]
train_song2vec(in_file, out_file)