triandicAnt / Graphembeddingrecommendationsystem
Licence: mit
Python based Graph Propagation algorithm, DeepWalk to evaluate and compare preference propagation algorithms in heterogeneous information networks from user item relation ship.
Stars: ✭ 144
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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
https://github.com/phanein/deepwalk
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