dltkddn0525 / MAML

MAML pytorch implementation

User Diverse Preferences Modeling By Multimodal Attentive Metric Learning

Pytorch implementation of User Diverse Preferences Modeling By Multimodal Attentive Metric Learning(Liu et al., 2019)

Data preparation

Data should be prepared as follows

• train.csv (Rating data for train)
• test.csv (Rating data for test)
• image_feature.npy (Image features of items extracted from pretrained network)
• doc2vecFile (Parameters of model pretrained with text data of items)

Usage

# Train & Test Top 10 Recommendation
CUDA_VISIBLE_DEVICES=0 python main.py --save_path <Your save path> --data_path <Your data path> --top_k 10

The following results will be saved in <Your save path>

• train.log ( epoch, total loss, embedding loss, feature loss, covariance loss )
• test.log ( epoch, hit ratio, nDCG )
• model.pth (model saved every 100 epoch)

Arguments

Argument	Type	Description	Default	Paper
save_path	str	Path to save result	'./result'	-
data_path	str	Path to dataset	'./Data/Office'	-
batch_size	int	Train batch size	1024	-
epoch	int	Train epoch	1000	maximum 1000
embed_dim	int	Dimension of latent vectors	64	64
dropout_rate	float	Dropout rate in feature fusion network	0.2	-
lr	float	Learning rate	0.001	0.0001~0.1
margin	float	Margin for embedding loss	1.6	1.6
feat_weight	float	Weight of feature loss	7	7
cov_weight	float	Weight of covariance loss	5	5
top_k	int	Top k Recommendation	10	10
num_neg	int	Number of negative samples for training	4	4

Result

Reference

Official Code