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Experiment Settings

Dataset: MovieLens-1M

Metircs: Precision@5, Recall@5, HR@5, nDCG@5,MRR@5

Task defination: We format the dataset into tasks and each user can be represented as a task. Therefore, we set the task proportion of training: validation: test as 8:1:1. For each task, we randomly select 10 interactions as query set, and the others as support set.

Data type and filtering: We use MovieLens-1M for the rating and click experiment respectively. For rating settings, we use the original rating scores. For click settings, as many papers do, we consider rating scores equal or above 4 as positive labels, and others are negative. Moreover, we set the user interaction number interval as [13,100] as many papers do.

The common configurations are listed as follows.

# Dataset config
USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id

load_col:
    inter: [user_id, item_id, rating]
    item: [item_id,movie_title,release_year,class]
    user: [user_id,age,gender,occupation,zip_code]
user_inter_num_interval: [13,100]

# Training and evaluation config
epochs: 10
train_batch_size: 32
valid_metric: mrr@5

# Evaluate config
eval_args:
    group_by: task
    order: RO
    split: {'RS': [0.8,0.1,0.1]}
    mode : labeled

# Meta learning config
meta_args:
    support_num: none
    query_num: 10

# Metrics
metrics: ['precision','recall','hit','ndcg','mrr']
metric_decimal_place: 4
topk: 5

Hyper Parameter Tuning

Model Best Hyper Parameter Tuning Range
FOMeLU embedding_size: [8];
train_batch_size: [8];
lr: [0.01];
mlp_hidden_size: [[64,64]]		 embedding_size: [8,16,32,64,128,256];
train_batch_size: [8,16,32,64,128,256];
lr: [0.0001,0.001,0.01,0.05,0.1,0.2,0.5,1.0];
mlp_hidden_size: [[8,8],[16,16],[32,32],[64,64],[128,128],[256,256]]
MAMO embedding: [16];
train_batch_size: [8];
lambda (lr): [0.01];
beta: [0.05]		 embedding: [8,16,32,64,128,256];
train_batch_size: [8,16,32,64,128,256];
lambda (lr): [0.0001,0.001,0.01,0.05,0.1,0.2,0.5,1.0];
beta: [0.05,0.1,0.2,0.5,0.8,1.0]
TaNP embedding: [16];
train_batch_size: [8];
lr: [0.01];
lambda: [1.0]		 embedding: [8,16,32,64,128,256];
train_batch_size: [8,16,32,64,128,256];
lr: [0.0001,0.001,0.01,0.05,0.1,0.2,0.5,1.0];
lambda: [0.05,0.1,0.2,0.5,0.8,1.0]
LWA embedding_size: [8];
train_batch_size: [8];
lr: [0.2];
embeddingHiddenDim: [64]		 embedding_size: [8,16,32,64,128,256];
train_batch_size: [8,16,32,64,128,256];
lr: [0.0001,0.001,0.01,0.05,0.1,0.2,0.5,1.0];
embeddingHiddenDim: [8,16,32,64,128,256]
NLBA embedding_size: [8];
train_batch_size: [8];
lr: [0.01];
recHiddenDim: [8]		 embedding_size: [8,16,32,64,128,256];
train_batch_size: [8,16,32,64,128,256];
lr: [0.0001,0.001,0.01,0.05,0.1,0.2,0.5,1.0];
recHiddenDim: [8,16,32,64,128,256]
MetaEmb embedding_size: [256];
train_batch_size: [8];
lr: [0.5];
alpha: [0.5]		 embedding_size: [8,16,32,64,128,256];
train_batch_size: [8,16,32,64,128,256];
lr: [0.0001,0.001,0.01,0.05,0.1,0.2,0.5,1.0];
alpha: [0.05,0.1,0.2,0.5,0.8,1.0]
MWUF embedding_size: [256];
train_batch_size: [64];
warmLossLr: [0.05];
indexEmbDim: [128]		 embedding_size: [8,16,32,64,128,256];
train_batch_size: [8,16,32,64,128,256];
warmLossLr: [0.0001,0.001,0.01,0.05,0.1,0.2,0.5,1.0];
indexEmbDim: [8,16,32,64,128,256]