{"id":20317317,"url":"https://github.com/mahikshith/rec_sys_with_auto_encoder","last_synced_at":"2025-07-15T07:14:00.202Z","repository":{"id":251636316,"uuid":"837988629","full_name":"mahikshith/Rec_Sys_with_auto_encoder","owner":"mahikshith","description":null,"archived":false,"fork":false,"pushed_at":"2024-08-04T16:24:16.000Z","size":877,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-14T13:16:32.563Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/mahikshith.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":"2024-08-04T16:15:49.000Z","updated_at":"2024-08-04T16:24:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"294df42d-f00f-479b-940d-14e378c0090a","html_url":"https://github.com/mahikshith/Rec_Sys_with_auto_encoder","commit_stats":null,"previous_names":["mahikshith/rec_sys_with_auto_encoder"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mahikshith%2FRec_Sys_with_auto_encoder","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mahikshith%2FRec_Sys_with_auto_encoder/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mahikshith%2FRec_Sys_with_auto_encoder/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mahikshith%2FRec_Sys_with_auto_encoder/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mahikshith","download_url":"https://codeload.github.com/mahikshith/Rec_Sys_with_auto_encoder/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241818900,"owners_count":20025212,"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":[],"created_at":"2024-11-14T18:30:55.249Z","updated_at":"2025-03-04T09:16:08.683Z","avatar_url":"https://github.com/mahikshith.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Introduction : \n\nThis project implements a movie recommender system using Autoencoders. The core idea is to learn a compressed representation (latent space) of user-movie interactions, which can be used to predict user preferences for unseen movies.\n\nAutoencoder Architecture\n\nAn Autoencoder is a neural network architecture composed of two main components:\n\nEncoder: Maps input data (user-movie ratings matrix) to a lower-dimensional latent space representation.\nDecoder: Reconstructs the original input data from the latent space representation.\n\nFeature Extraction:\n\nThe encoder part of the Autoencoder is crucial for extracting features from the user-movie rating matrix.\nBy training the Autoencoder to reconstruct the original ratings matrix, it learns to capture underlying patterns and relationships between users and movies.\nThe latent space representation generated by the encoder acts as a feature map, containing compressed information about users and movies.\n\nKey Components\n\nUser-Item Matrix: A sparse matrix representing user ratings for different movies.\nAutoencoder Model: A neural network architecture with an encoder and decoder.\nLatent Space: The compressed representation of user-movie interactions generated by the encoder.\nRecommendation Generation: Using the learned latent space, similar users or movies can be identified, and recommendations can be made based on their preferences.\n\nSteps Involved\n\nAutoencoder Training: Train the Autoencoder to reconstruct the user-movie rating matrix.\nFeature Extraction: Extract the latent space representations from the trained encoder.\nSimilarity Calculation: Calculate similarity between users or movies based on their latent representations.\nRecommendation Generation: Recommend movies to users based on the similarity scores.\n\n\nAutoencoders learn efficient data representations.\nLatent space captures underlying patterns in user-movie interactions.\nRecommender systems predict user preferences based on latent features.\nFeature extraction is performed by the encoder part of the Autoencoder.\nSimilarity between users or movies is calculated in the latent space.\nEvaluation metrics assess the performance of the recommender system.\nIncorporating additional features can enhance recommendation accuracy.\nExperiment with different Autoencoder architectures for improved results.\n Combine Autoencoders with other recommendation techniques for hybrid approaches.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmahikshith%2Frec_sys_with_auto_encoder","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmahikshith%2Frec_sys_with_auto_encoder","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmahikshith%2Frec_sys_with_auto_encoder/lists"}