README.md

MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation

Official code implementation for ICDE 23 paper MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation.

[Paper]
[Slides]

Requirements

tensorflow-gpu==1.12.0
requests==2.26.0
tqdm==4.62.2
pandas==1.1.5
scikit-learn==0.24.2
numpy==1.16.6
deepctr==0.9.0

• RTX 2080 + 64G RAM
• python: 3.6
• Ubuntu 20.04

Data Preprocess

Amazon dataset

Enter dataset/Amazon

raw dataset at：https://nijianmo.github.io/amazon/index.html#complete-data

1. change the split rule in config_*.json.
2. run split.py --config config_*.json. It will automatically download and split the domains.

Taobao dataset

Enter dataset/Taobao

1. download dataset
2. unzip the dataset into raw_data
   1. theme_click_log.csv
   2. theme_item_pool.csv
   3. user_item_purchase_log.csv
   4. item_embedding.csv
   5. user_embedding.csv
3. change the dataset config in config_*.json. theme_num = -1 denotes using all domains.
4. run split.py --config config_*.json to create dataset.

Preprocessed dataset

You can download the datasets and extract the folders into dataset/Amazon and dataset/Taobao, respectively.

• Amazon-6/13
• Taobao-10/20/30

Run experiments

Run baselines

• change model name in config.json

python3 run.py --config config/Taobao-10/deepctr.json

Run Domain Negotiation

python3 run.py --config config/Taobao-10/deepctr_DN.json

Run MAMDR

python3 run.py --config config/Taobao-10/deepctr_DN+DR.json

Config Description

Model Name：

basemodel_extenstion(s)

Base model：

single domain： mlp, wdl, nfm, autoint, deepfm

multi tasks： shared_bottom, mmoe, ple

multi domain： star

Extenstion：

default: joint learning
separate: separatly train for each domain
finetune: finetune after joint training
meta: training using MAML
reptile: reptile meta learning
mldg: MLDG
uncertainty_weight: weighted loss
pcgrad: pcgrad        
domain_negotiation: domain negotiation meta learning
mamdr: DN + DR       
batch: using batch for meta learning

Example

• MLP + Joint:mlp
• DeepFM + Joint: deepfm
• MLP + Joint + Finetune:mlp_finetune
• MLP + MAML:mlp_meta_finetune
• MLP + DN: mlp_meta_domain_negotiation_finetune_
• MLP + DN + DR : mlp_meta_mamdr_finetune

You can find more examples in config/.

Config example

{
  "model": {
    "name": "mlp_meta_mamdr_finetune", // model name
    "norm": "none", // noarmalization method for star: none, bn, pn
    "dense": "dense", // dense for star: dense, star
    "auxiliary_net": false,
    "user_dim": 128,
    "item_dim": 128,
    "domain_dim": 128,
    "auxiliary_dim": 128,
    "hidden_dim": [ // hidden sizes for hidden layers
      256,
      128,
      64
    ],
    "dropout": 0.5
  },
  "train": {
    "load_pretrain_emb": true, // whether load pretrain embding, only support for Taobao
    "emb_trainable": false, // whether train embedding
    "epoch": 99999,
    "learning_rate": 0.001, // inner learning rate
    "meta_learning_rate": 0.1, // outer learning rate
    "domain_meta_learning_rate": 0.1, // Not used
    "merged_method": "plus", 
    "sample_num": 5, // sample number for DR
    "add_query_domain": true,
    "finetune_every_epoch": false,
    "shuffle_sequence": true, // whether shuffle the domain sequence
    "meta_sequence": "random",
    "target_domain": -1, // disabled
    "domain_regulation_step": 0, // disabled
    "meta_train_step": 0, // disabled
    "meta_finetune_step": 0, // disabled
    "meta_split": "train-train", // how to split the query and support set for meta-learning: train-train, meta-train/val, meta-train/val-no-exclusive
    "meta_split_ratio": 0.8,
    "average_meta_grad": "none",
    "meta_parms": [
      "all"
    ],
    "result_save_path": "result",
    "checkpoint_path": "checkpoint",
    "loss": "binary_crossentropy",
    "optimizer": "adam",
    "patience": 3,
    "val_every_step": 1,
    "histogram_freq": 0,
    "shuffle_buff_size": 10000
  },
  "dataset": {
    "name": "Taobao",
    "dataset_path": "dataset/Taobao",
    "domain_split_path": "split_by_theme_10",
    "batch_size": 1024,
    "shuffle_buffer_size": 10000,
    "num_parallel_reads": 8,
    "seed": 123
  }
}