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基于Topic Sentiment特征的深度学习推荐模型.zip
共156个文件
pth:56个
py:55个
pyc:20个
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基于Topic Sentiment特征的深度学习推荐模型 深度学习(Deep Learning,简称DL)是机器学习(Machine Learning,简称ML)领域中一个新的研究方向,其目标是让机器能够像人一样具有分析学习能力,识别文字、图像和声音等数据。深度学习通过学习样本数据的内在规律和表示层次,使机器能够模仿视听和思考等人类活动,从而解决复杂的模式识别难题。 深度学习的核心是神经网络,它由若干个层次构成,每个层次包含若干个神经元。神经元接收上一层次神经元的输出作为输入,通过加权和转换后输出到下一层次神经元,最终生成模型的输出结果。神经网络之间的权值和偏置是神经网络的参数,决定了输入值和输出值之间的关系。 深度学习的训练过程通常涉及反向传播算法,该算法用于优化网络参数,使神经网络能够更好地适应数据。训练数据被输入到神经网络中,通过前向传播算法将数据从输入层传递到输出层,然后计算网络输出结果与实际标签之间的差异,即损失函数。通过反向传播算法,网络参数会被调整以减小损失函数值,直到误差达到一定的阈值为止。 深度学习中还包含两种主要的神经网络类型:卷积神经网络(Convoluti
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基于Topic Sentiment特征的深度学习推荐模型.zip (156个子文件)
software_reviews.dict 23KB
magazines_reviews.dict 21KB
.gitignore 50B
Senti-based-Deep-Learning-Recommendation.iml 291B
0423_with-sentiment_lr.model 21KB
0423_normal_lr.model 20KB
0517_magazine_with-sentiment_lr.model 9KB
0509_software_with-sentiment_lr.model 8KB
0517_magazine_normal_lr.model 8KB
0509_software_normal_lr.model 8KB
0518_with-sentiment_conti_software_wide_deep_model_epoch_324.pth 1.88MB
0509_with-sentiment_conti_software_wide_deep_model_epoch_4.pth 1.88MB
0510_with-sentiment_magazine_wide_deep_model_epoch_436.pth 1.88MB
0507_normal_software_wide_deep_model_epoch_1061.pth 1.86MB
0518_normal_conti_software_wide_deep_model_epoch_0.pth 1.86MB
0518_normal_conti_software_wide_deep_model_epoch_1.pth 1.86MB
0510_normal_conti_magazine_wide_deep_model_epoch_1.pth 1.86MB
0421_with-sentiment_conti_amazon_nfm_model_epoch_7.pth 1.55MB
0415_with-sentiment_conti_amazon_nfm_model_epoch_2.pth 1.55MB
0419_with-sentiment_amazon_nfm_model_epoch_38.pth 1.55MB
0419_with-sentiment_conti_amazon_nfm_model_epoch_70.pth 1.55MB
0419_with-sentiment_conti_amazon_nfm_model_epoch_2.pth 1.55MB
0428_with-sentiment_amazon_deepfm_model_epoch_30.pth 1.54MB
0428_normal_amazon_deepfm_model_epoch_22.pth 1.52MB
0421_normal_conti_amazon_nfm_model_epoch_90.pth 1.51MB
0423_normal_conti_amazon_nfm_model_epoch_6.pth 1.51MB
0418_normal_conti_amazon_nfm_model_epoch_575.pth 1.51MB
0413_with-sentiment_conti_amazon_wide_deep_model_epoch_400.pth 1.29MB
0413_with-sentiment_conti_amazon_wide_deep_model_epoch_30.pth 1.29MB
0412_with-sentiment_amazon_wide_deep_model_epoch_100.pth 1.29MB
0412_normal_amazon_wide_deep_model_epoch_200.pth 1.27MB
0413_normal_conti_amazon_wide_deep_model_epoch_13.pth 1.27MB
0411_normal_conti_amazon_wide_deep_model_epoch_0.pth 1.27MB
0421_with-sentiment_amazon_wide_deep_model_epoch_132.pth 1.27MB
0421_normal_amazon_wide_deep_model_epoch_168.pth 1.25MB
0415_complex_normal_amazon_wide_deep_model_epoch_159.pth 1.24MB
0408_with-sentiment_amazon_nfm_model_epoch_0.pth 1.05MB
0408_with-sentiment_conti_amazon_nfm_model_epoch_0.pth 1.05MB
0408_with-sentiment_conti_amazon_nfm_model_epoch_100.pth 1.05MB
0408_with-sentiment_amazon_nfm_model_epoch_100.pth 1.05MB
0408_normal_amazon_nfm_model_epoch_200.pth 1.03MB
0407_normal_amazon_nfm_model_epoch_0.pth 1.03MB
0316_normal_amazon_nfm_model_epoch_100.pth 1.02MB
0422_with-sentiment_amazon_wide_deep_model_epoch_104.pth 942KB
0422_with-sentiment_amazon_wide_deep_model_epoch_76.pth 942KB
0415_with-sentiment_conti_amazon_wide_deep_model_epoch_206.pth 938KB
0423_normal_amazon_wide_deep_model_epoch_110.pth 920KB
0422_normal_conti_amazon_wide_deep_model_epoch_6.pth 920KB
0415_normal_amazon_wide_deep_model_epoch_116.pth 918KB
0513_with-sentiment_software_deepfm_model_epoch_37.pth 897KB
0513_normal_software_deepfm_model_epoch_128.pth 875KB
0517_normal_software_deepfm_model_epoch_73.pth 875KB
0517_normal_magazine_deepfm_model_epoch_43.pth 875KB
0510_with-sentiment_magazine_nfm_model_epoch_94.pth 688KB
0508_with-sentiment_software_nfm_model_epoch_563.pth 685KB
0518_with-sentiment_conti_software_nfm_model_epoch_13.pth 685KB
0509_normal_conti_software_nfm_model_epoch_189.pth 643KB
0517_normal_conti_magazine_nfm_model_epoch_5.pth 638KB
0518_normal_conti_magazine_nfm_model_epoch_764.pth 638KB
0510_normal_magazine_nfm_model_epoch_870.pth 638KB
0311_with-sentiment_nfm_model_epoch_100.pth 267KB
0312_with-sentiment_conti_nfm_model_epoch_400.pth 267KB
0313_with-sentiment_conti_nfm_model_epoch_500.pth 267KB
0312_normal_conti_nfm_model_epoch_900.pth 237KB
0311_normal_nfm_model_epoch_100.pth 237KB
0311_normal_conti_nfm_model_epoch_700.pth 237KB
train_sentiment_model.py 26KB
train_sentiment_model_others.py 17KB
train_sentiment_model_cloud_version.py 13KB
see_details_of_columns.py 12KB
train_model.py 12KB
generate_corpus.py 11KB
deep_cross_model.py 10KB
deep_cross_model_magazine.py 10KB
train_sentiment_model_tfversion.py 10KB
Wide_Deep.py 9KB
TrainSentimentModels.py 9KB
BERT_ProcessData.py 9KB
DicBased.py 7KB
main_CF.py 7KB
FM.py 7KB
random_select_get_topics_cloud_Version.py 6KB
cal_sentiment_feature_about_user_item_amazon_v2.0.py 5KB
main.py 5KB
random_select_get_topics.py 5KB
do_reviews_analysis_amazon.py 5KB
do_reviews_analysis.py 4KB
LR.py 4KB
do_eval_models.py 4KB
do_reviews_analysis_general.py 4KB
get_topics_bydict.py 4KB
deepfm_model.py 3KB
evaluate_moreIndicators.py 3KB
evaluate_moreIndicators.py 3KB
layer.py 3KB
get_description_topics.py 3KB
get_topics.py 3KB
generate_traindata.py 2KB
evaluate_moreIndicators.py 2KB
get_inter_data.py 2KB
共 156 条
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