{"id":22752043,"url":"https://github.com/zeuscoderbe/recommender-system","last_synced_at":"2025-08-09T12:47:40.547Z","repository":{"id":230228030,"uuid":"778823304","full_name":"ZeusCoderBE/Recommender-System","owner":"ZeusCoderBE","description":"Content-based Filtering, Neighborhood-based Collaborative Filtering","archived":false,"fork":false,"pushed_at":"2024-06-05T03:45:10.000Z","size":2972,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-09T12:47:35.562Z","etag":null,"topics":["collaborative-filtering","content-based-recommendation","hybrid-recommender-system","knn-regression","machine-learning"],"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/ZeusCoderBE.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":"2024-03-28T13:31:16.000Z","updated_at":"2025-08-04T00:46:54.000Z","dependencies_parsed_at":"2024-06-05T04:44:31.817Z","dependency_job_id":null,"html_url":"https://github.com/ZeusCoderBE/Recommender-System","commit_stats":null,"previous_names":["zeuscoderbe/recommender-system"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ZeusCoderBE/Recommender-System","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FRecommender-System","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FRecommender-System/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FRecommender-System/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FRecommender-System/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ZeusCoderBE","download_url":"https://codeload.github.com/ZeusCoderBE/Recommender-System/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FRecommender-System/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269579524,"owners_count":24441365,"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","status":"online","status_checked_at":"2025-08-09T02:00:10.424Z","response_time":111,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["collaborative-filtering","content-based-recommendation","hybrid-recommender-system","knn-regression","machine-learning"],"created_at":"2024-12-11T05:09:25.532Z","updated_at":"2025-08-09T12:47:40.513Z","avatar_url":"https://github.com/ZeusCoderBE.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Recommendation System\n\n## Introduction\n\n### Environment Setup\n1. Install Python libraries: `numpy`, `scikit-learn`,`pandas`.\n2. Use data from the https://www.kaggle.com/datasets/prajitdatta/movielens-100k-dataset.\n\n### Data\nThe data used includes user information (age, gender, occupation),movies(Title,Genre) and their ratings for movies. This data is split into training(ua.base) and test(ua.test) sets.\n### The main idea of the two algorithms Content Filtering and Collaborative Filtering\n- Content Filtering :Suggest items based on the user's profile or based on the content/attributes of items similar to items the user has selected in the past.\n  \n![image](https://github.com/ZeusCoderBE/Recommender-System/assets/117000361/2714e86a-b43a-4bc2-b9ba-fc125739931e)\n\n- Collaborative Filtering: Suggest items based on similarity between users and/or items. It can be understood that this is a way to suggest a user based on users with similar behavior.\n\n![image](https://github.com/ZeusCoderBE/Recommender-System/assets/117000361/ed58ed37-ef56-4d87-bd19-38f33bcfa057)\n\n\n### I implemented two recommendation algorithms: Content Filtering and Collaborative Filtering.\n\n1. Content Filtering:\n\n    - I created a vector representation for each movie using TF- IDF (item profiles). \n\n    - I trained a ridge regression model for each user to learn the weights(user profiles).\n\n    - I used  item profiles and user profiles to predict and recommend movie ratings.\n\n2. Collaborative Filtering:\n\n   - I utilized two approaches: item-item and user-user.\n\n   - I calculated cosine similarity between items or users.\n\n   - I implemented a KNN model by selecting K similar users/items to predict rating scores.\n    \n3. Hybrid between collaborative filtering and content filtering\n\n  - After predicting the rating in the test set, I combined the predicted rating in the two algorithm\n\n  - I reevaluated using the RMSE measure\n\n### Libraries and Technologies\n- **Programming Language:** Python\n- **Main Libraries:** NumPy, scikit-learn,pandas\n- **Model:** Ridge Regression, TF-IDF Transformer,KNN User-User,KNN Item-Item\n\n### Performance Evaluation\n- Utilize Root Mean Squared Error (RMSE) to assess the accuracy of the model on the test set.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzeuscoderbe%2Frecommender-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzeuscoderbe%2Frecommender-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzeuscoderbe%2Frecommender-system/lists"}