# ACAM-model

**Repository Path**: fs57585/ACAM-model

## Basic Information

- **Project Name**: ACAM-model
- **Description**: It is deep recommendation model with attribute-level co-attention
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-25
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ACAM model
* Usage
 `python3 main.py`

* structure
```
.
├── code
│   ├── dataset_build.py
│   ├── main.py
│   ├── metrics.py
│   ├── model.py
│   └── train.py
├── data
│   ├── movie
│   │   ├── data
│   │   └── pretrained_embeddings
│   └── music
│       ├── data
│       └── pretrained_embeddings
└── README.md
```

model.py：The main part of the model, it is realized by pytorch.

train.py：Build model and train the parameters in the model.

metrics.py：Define various evaluation functions(prec, ap, ndcg and rr).

dataset_build.py：Build train set and test set for model.

You can run the model on music dataset by 
```
python3 --dataset music
```

You can run the model on douban movie dataset by
```
python3 --dataset movie
```

Other important parameters are explained as follows:

batch_size:The amount of data for each epoch.

L:The number of history records for each user.

use_KGloss:Whether to use knowledge loss founction.

learning_rate:The learning rate for training model.

method:Which model to used.

d:The dimension for embedding vectors.