# Graph-Embedding-For-Recommendation-System

Python based Graph Propagation algorithm, DeepWalk to evaluate and compare preference propagation algorithms in heterogeneous information networks from user item relation ship.

## Objective:

- Predict User's preference for some items, they have not yet rated using graph based Collaborative Filtering technique, DeepWalk on user-movie rating data set.
- Firstly, using the movie review data set, a heterogeneous graph network with nodes as users, movies and its associated entities (actors, directors) were created.
- DeepWalk was used to generate a random walk over this graph.
- Theses random walks were embedded in low dimensional space using Word2Vec.
- The prediction for rating for a user-movie pair was done by finding the movie-rating node with the highest similarity to the user node.

## Requirements:

- numpy
- scipy

## Steps to Run:

Run the following command from root folder(not inside rec2vec)

```
python -m rec2vec --walk-length 2 --number-walks 2 --workers 4
# ****arguments****
# walk-length
# number-walks
# workers
```

#### Ref : https://github.com/phanein/deepwalk

# Graphembeddingrecommendationsystem

## Python based Graph Propagation algorithm, DeepWalk to evaluate and compare preference propagation algorithms in heterogeneous information networks from user item relation ship.

### Graphembeddingrecommendationsystem Info

⭐ Stars 131

🔗 Source Code github.com

🕒 Last Update 10 months ago

🕒 Created 5 years ago

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😎 Author triandicAnt