{"id":22695852,"url":"https://github.com/surajiyer/recommender-systems","last_synced_at":"2025-08-12T16:40:14.761Z","repository":{"id":98199841,"uuid":"124047769","full_name":"surajiyer/Recommender-Systems","owner":"surajiyer","description":"Recommender systems, 2017-18","archived":false,"fork":false,"pushed_at":"2018-09-01T04:35:17.000Z","size":6446,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-29T18:11:12.610Z","etag":null,"topics":["cbow","cifar100","cnn","nlp","recommender-system","siamese-network","skipgram","vgg16"],"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/surajiyer.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":"2018-03-06T08:42:03.000Z","updated_at":"2018-09-01T04:35:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"f9a40b7b-f91c-4837-a3b2-304c16059585","html_url":"https://github.com/surajiyer/Recommender-Systems","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/surajiyer/Recommender-Systems","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/surajiyer%2FRecommender-Systems","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/surajiyer%2FRecommender-Systems/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/surajiyer%2FRecommender-Systems/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/surajiyer%2FRecommender-Systems/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/surajiyer","download_url":"https://codeload.github.com/surajiyer/Recommender-Systems/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/surajiyer%2FRecommender-Systems/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270099279,"owners_count":24527027,"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":"2025-08-12T02:00:09.011Z","response_time":80,"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":["cbow","cifar100","cnn","nlp","recommender-system","siamese-network","skipgram","vgg16"],"created_at":"2024-12-10T04:12:07.796Z","updated_at":"2025-08-12T16:40:14.631Z","avatar_url":"https://github.com/surajiyer.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Recommender systems, 2017-18\n\nThis repository contains notebooks and other files associated with my class on *Recommender systems* from Eindhoven University of Technology.\n\n- Section 1: Learning CBoW and Skipgram models (with and without negative sampling) to generate words embeddings. Applied to analogy detection and sentence reconstruction.\n\n- Section 2: Learning about neural codes (link to paper: https://arxiv.org/pdf/1404.1777.pdf) to generate image embeddings with and without PCA compression. Three different ways to generate neural codes: Convolutional networks, Denoising Autoencoders, Sparse Autoencoders. Applied to image retrieval with Nearest neighbor detection using neural codes as feature space.\n\n- Section 3: \n  - Learning about Siamese networks \u0026 one-shot learning. Training a siamese network on 80 classes from Cifar-100 dataset, then use neural codes to perform multiple N-way one-shot learning tasks on remaining 20 classes. Model performs better than random guessing.\n  - Learning about LSTM, GRU, and Bidirectional variants. Applied in generating document sequence embeddings based on binary sentiment classification task. Also used generated embeddings to one-shot learning task on Amazon product reviews dataset.\n  - Combining the knowledge of Siamese networks and RNNs to Image-Caption retrieval problem. Preprocessed the data to create triplet instances [\u003cimage, positive caption, negative caption\u003e, ...]. Designed a NN architecture that generates image and caption neural codes, combines them, and optimizes a max-margin loss function to increase the bounds between the positive pair embedding and negative pair embedding. Laslty, we use the *kNN* algorithm to recommend top-k nearest images given a caption or top-k nearest captions given an image.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsurajiyer%2Frecommender-systems","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsurajiyer%2Frecommender-systems","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsurajiyer%2Frecommender-systems/lists"}