dsr's Introduction

DSR

Introduction

Deep Social Recommendation (DSR) is a new recommendation framework tailored to knowledge graph-based personalized recommendation. DSR fully exploits both the social influence from potential friends and the collaborative influence from interactions for better embedding learning.

Environment Requirement

Python == 3.7
numpy == 1.16.2

Datasets

Ciao & Epinion
- In the Ciao and Epinion datasets, we have user' ratings towards items. The data is saved in a txt file (rating.txt) and the format is as follows:
```
userid itemid rating
```
- trust.txt: it contains the trust relations between users. There are two columns and both of them are userid, denoting there is a social relation between two users.
```
userid userid
```
- train.txt: it contains data for train. Each line is a user with a list of her interacted items.
- test.txt: it contains data for test. Each line is a user with a list of her test items.

Model

Deep Social Collaborative Ranking (DSCR.py)
- Model for Deep Social Collaborative Ranking
Running Command
```
   python3 DSCR.py --dataset ciao --regs [1e-5] --embed_size 64 --layer_size [64,64,64] --layer_size_S [64,64] --lr 0.0005 --batch_size 1024 --epoch 400 
   
   python3 DSCR.py --dataset epinion --regs [1e-5] --embed_size 64 --layer_size [64,64,64] --layer_size_S [64,64] --lr 0.0005 --batch_size 1024 --epoch 400 
```
You need to specify serveral parameters for training and testing:
- dataset: ciao / epinion
- regs: regularization weight
- layer_size: the number of layers and embedding size for user-item interaction network
- layer_size_s : the number of layers and embedding size for user-user social network
- lr: learning rate
- batch_size : the size of batch for training
- epoch : the epoch for training

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