{"id":25346561,"url":"https://github.com/lsjsj92/recommender_system_with_python","last_synced_at":"2025-04-08T03:11:25.697Z","repository":{"id":41256833,"uuid":"236397944","full_name":"lsjsj92/recommender_system_with_Python","owner":"lsjsj92","description":"recommender system tutorial with Python","archived":false,"fork":false,"pushed_at":"2024-06-03T12:23:39.000Z","size":621,"stargazers_count":199,"open_issues_count":1,"forks_count":98,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-31T17:18:12.744Z","etag":null,"topics":["chatgpt","collaborative-filtering","machine-learning","matrix-factorization","openai","python","recommendation-engine","recommendation-system","recommendations","recommender","recommender-system"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lsjsj92.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-01-27T00:46:30.000Z","updated_at":"2025-03-26T16:02:53.000Z","dependencies_parsed_at":"2024-06-03T14:21:40.116Z","dependency_job_id":"e3d5fdeb-2ef0-40f0-ac42-01d822bed4d9","html_url":"https://github.com/lsjsj92/recommender_system_with_Python","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/lsjsj92%2Frecommender_system_with_Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lsjsj92%2Frecommender_system_with_Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lsjsj92%2Frecommender_system_with_Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lsjsj92%2Frecommender_system_with_Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lsjsj92","download_url":"https://codeload.github.com/lsjsj92/recommender_system_with_Python/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247767236,"owners_count":20992548,"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":["chatgpt","collaborative-filtering","machine-learning","matrix-factorization","openai","python","recommendation-engine","recommendation-system","recommendations","recommender","recommender-system"],"created_at":"2025-02-14T13:38:02.136Z","updated_at":"2025-04-08T03:11:25.677Z","avatar_url":"https://github.com/lsjsj92.png","language":"Jupyter Notebook","readme":"\n# 파이썬을 활용한 추천 시스템 구현(recommender system with Python)\n\n### 각 파일에 대한 자료 설명\n\n\u003e 각 파일에 대한 설명은 https://lsjsj92.tistory.com/ 블로그에 올려두었습니다. 상세주소는 각 파일 최상단에 있으니 참고바랍니다.\n\n**1. recommender system basic**\n- 추천 시스템 기본 유형 소개 : 이론\n    - content based filtering\n    - collaborative filtering\n\n    \n**2. recommender system basic with Python - 1 content based filtering**\n- 파이썬을 활용해 content based filtering 구현\n- kaggle의 movies dataset 활용\n\n\n**3. recommender system basic with Python - 2 Collaborative Filtering**\n- 파이썬을 활용해 collaborative filtering 구현\n- kaggle의 movies dataset, movielens dataset 활용\n\n\n**4. recommender system basic with Python - 3 Matrix Factorization**\n- 파이썬을 활용해 Matrix Factorization 구현 및 이론 설명\n- kaggle의 movies dataset, movielens dataset 활용\n\n\n**5. naver news recommender**\n- Naver news 데이터를 활용해 추천 시스템 적용\n- Doc2vec 등의 embedding 방법을 사용\n\n**6. deep learning recommender system**\n- 딥러닝 기반의 추천 시스템 활용 예제 코드\n- Keras 활용\n\n\n**7. 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