{"id":13788789,"url":"https://github.com/triandicAnt/GraphEmbeddingRecommendationSystem","last_synced_at":"2025-05-12T03:30:54.956Z","repository":{"id":67655408,"uuid":"56817078","full_name":"triandicAnt/GraphEmbeddingRecommendationSystem","owner":"triandicAnt","description":" Python based Graph Propagation algorithm, DeepWalk to evaluate and compare preference propagation algorithms in heterogeneous information networks from user item relation ship.","archived":false,"fork":false,"pushed_at":"2018-02-04T06:42:58.000Z","size":22525,"stargazers_count":189,"open_issues_count":0,"forks_count":54,"subscribers_count":10,"default_branch":"master","last_synced_at":"2024-11-18T02:38:58.368Z","etag":null,"topics":["deepwalk","graph","graph-embedding","graph-propagation-algorithm","prediction","python","rating","recommendation-system"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/triandicAnt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2016-04-22T01:34:07.000Z","updated_at":"2024-10-19T12:47:21.000Z","dependencies_parsed_at":"2023-04-26T07:02:45.560Z","dependency_job_id":null,"html_url":"https://github.com/triandicAnt/GraphEmbeddingRecommendationSystem","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/triandicAnt%2FGraphEmbeddingRecommendationSystem","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/triandicAnt%2FGraphEmbeddingRecommendationSystem/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/triandicAnt%2FGraphEmbeddingRecommendationSystem/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/triandicAnt%2FGraphEmbeddingRecommendationSystem/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/triandicAnt","download_url":"https://codeload.github.com/triandicAnt/GraphEmbeddingRecommendationSystem/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253667947,"owners_count":21944943,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deepwalk","graph","graph-embedding","graph-propagation-algorithm","prediction","python","rating","recommendation-system"],"created_at":"2024-08-03T21:00:53.581Z","updated_at":"2025-05-12T03:30:49.939Z","avatar_url":"https://github.com/triandicAnt.png","language":"Python","readme":"# Graph-Embedding-For-Recommendation-System\n Python based Graph Propagation algorithm, DeepWalk to evaluate and compare preference propagation algorithms in heterogeneous information networks from user item relation ship.\n\n## Objective:\n* 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. \n* Firstly, using the movie review data set, a heterogeneous graph network with nodes as users, movies and its associated entities (actors, directors) were created.\n* DeepWalk was used to generate a random walk over this graph. \n* Theses random walks were embedded in low dimensional space using Word2Vec. \n* 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.\n\n## Requirements:\n* numpy\n* scipy\n\n## Steps to Run:\nRun the following command from root folder(not inside rec2vec)\n```python\npython -m rec2vec --walk-length 2 --number-walks 2 --workers 4\n# ****arguments****\n# walk-length\n# number-walks\n# workers\n```\n\n#### Ref : https://github.com/phanein/deepwalk\n","funding_links":[],"categories":["TensorFlow Implementations"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FtriandicAnt%2FGraphEmbeddingRecommendationSystem","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FtriandicAnt%2FGraphEmbeddingRecommendationSystem","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FtriandicAnt%2FGraphEmbeddingRecommendationSystem/lists"}