# J2PRec

**Repository Path**: abc-pedicle/J2PRec

## Basic Information

- **Project Name**: J2PRec
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-15
- **Last Updated**: 2023-12-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# J2PRec
## Introduction 

Joint Product-Participant Recommendation (J2PRec) is a new framework for group-buying (GB) recommendation, which recommends both candidate products and participants for maximizing the success rate of a GB. J2PRec first encodes the various relations in GB for learning enhanced user and product embeddings. It then jointly learns the product and participant recommendation tasks under a probabilistic framework to maximize the GB likelihood.

## Environment Requirement

+ Python == 3.6.5. 
+ tensorflow == 1.14.0
+ numpy == 1.15.4
+ scipy == 1.1.0
+ sklearn == 0.20.0



## Datasets

+ social_relation.txt
   + Introduction: social relations among users. 
   + Format: (<user_id1> \t <user_id2>), denoting there is a social relation between the two users.
+ train_id.txt: 
   + Introduction: GB interactions for training. 
   + Format: (<initiator_id> \t <product_id> \t <participant_id1> \t ... \t <participant_idK>), denoting the initiator purchases the target product with the set of participants. 
+ tune.txt: 
   + Introduction: GB interactions for validation. 
   + Format: (<initiator_id> \t <product_id> \t <participant_id1> \t ... \t <participant_idK>)
+ test.txt:
   + Introduction: GB interactions for testing. 
   + Format: (<initiator_id> \t <product_id> \t <participant_id1> \t ... \t <participant_idK>)
+ test.negative.txt: 
   + Introduction: the sampled negative products for each test user. 
   + Format: (<user_id> \t <product_id1> <product_id2> ... <product_id1000>)
+ train.txt:
   + Introduction: the user and all her interacted products in training set. 
   + Format: (<user_id> <product_id1> <product_id2> ... <product_idM>)
+ Due to the file size limit, we provide an illustration example on brightkite dataset. The beibei and gowalla datasets can be downloaded here: https://drive.google.com/drive/folders/1PUUP7mA2xR-suP108jreXyOT-EcjnXBd?usp=sharing. 
   
## Running Command 

+ Data Preparation 

    Please run the data_preprocess.py in the Data folder to generate the required files. 

+ Model Training and Testing

    Please run the following example codes for model training and testing: 

         python3.6 Main.py --dataset brightkite --regs [1e-4,1e-4] --layer_size [64,64] --embed_size 64 --lr 0.001 --epoch 1000 --batch_size 1024 

    You need to specify serveral parameters for training and testing:
   
    + dataset: beibei / brightkite / gowalla
    + regs: regularization weight 
    + layer_size: the number of layers 
    + embed_size: embedding size 
    + lr: learning rate
    + batch_size : the size of batch for training
    + epoch : the epoch for training