{"id":19484038,"url":"https://github.com/akshaya13/recommendation-system","last_synced_at":"2026-05-18T11:04:33.497Z","repository":{"id":245492183,"uuid":"818407688","full_name":"akshaya13/recommendation-system","owner":"akshaya13","description":"Content Based Recommendation system using tags!","archived":false,"fork":false,"pushed_at":"2024-10-23T00:31:46.000Z","size":12602,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-16T17:06:23.950Z","etag":null,"topics":["nltk","scikit-learn","similarity-search","tmdb-database"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/akshaya13.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-06-21T19:36:12.000Z","updated_at":"2024-10-23T00:31:50.000Z","dependencies_parsed_at":null,"dependency_job_id":"b092d3c2-35b8-441a-b760-bde9ca557f00","html_url":"https://github.com/akshaya13/recommendation-system","commit_stats":null,"previous_names":["akshaya13/movie_recommendation_system","akshaya13/recommendation-system"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/akshaya13/recommendation-system","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akshaya13%2Frecommendation-system","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akshaya13%2Frecommendation-system/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akshaya13%2Frecommendation-system/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akshaya13%2Frecommendation-system/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/akshaya13","download_url":"https://codeload.github.com/akshaya13/recommendation-system/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akshaya13%2Frecommendation-system/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33175837,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-18T09:27:30.708Z","status":"ssl_error","status_checked_at":"2026-05-18T09:27:28.300Z","response_time":71,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["nltk","scikit-learn","similarity-search","tmdb-database"],"created_at":"2024-11-10T20:19:01.491Z","updated_at":"2026-05-18T11:04:33.464Z","avatar_url":"https://github.com/akshaya13.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Movie Recommendation System - Content-Based Recommendation System\n### Overview\n\nThis project implements a content-based movie recommendation system utilizing the TMDB 5000 dataset from Kaggle. The system analyzes various movie attributes to generate personalized recommendations based on user input.\n\n### Dataset\nSource: TMDB 5000 Dataset (https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata)\n\n* Version 1: Stemming + Bag of Words + Similarity Search\n* Version 2: Lemmatization + TFIDF + Similarity Search\n\n### Steps\n* Data Preprocessing: Clean and prepare the dataset for analysis.\n* Exploratory Data Analysis (EDA): Analyze the dataset to understand its structure and key features.\n* Feature Engineering: Extract meaningful features from the dataset to enhance recommendation accuracy.\n* Tag Creation: Generate tags based on multiple columns including Genre, Overview, Keywords, Cast, and Crew.\n* Text Processing: Apply stemming and lemmatization techniques, and remove stop words to refine the tags for better similarity matching.\n* Cosine similarity is applied to find the similar movies\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakshaya13%2Frecommendation-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakshaya13%2Frecommendation-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakshaya13%2Frecommendation-system/lists"}