{"id":28064302,"url":"https://github.com/nishanc/ml.net-productrecommendation","last_synced_at":"2026-04-20T04:03:19.879Z","repository":{"id":291800788,"uuid":"978827227","full_name":"nishanc/ML.NET-ProductRecommendation","owner":"nishanc","description":"This demo accompanies the tech talk \"Building Intelligent Applications with ML.NET\", where we explore how to use Matrix Factorization to build a product recommendation system based on co-purchase behavior.","archived":false,"fork":false,"pushed_at":"2025-05-06T18:21:33.000Z","size":3210,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-14T19:07:33.558Z","etag":null,"topics":["dotnet","microsoft","mldotnet","recommendation-system"],"latest_commit_sha":null,"homepage":"","language":"C#","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/nishanc.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-05-06T15:09:14.000Z","updated_at":"2025-05-06T18:21:36.000Z","dependencies_parsed_at":"2025-05-12T13:49:55.111Z","dependency_job_id":null,"html_url":"https://github.com/nishanc/ML.NET-ProductRecommendation","commit_stats":null,"previous_names":["nishanc/ml.net-productrecommendation"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nishanc/ML.NET-ProductRecommendation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nishanc%2FML.NET-ProductRecommendation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nishanc%2FML.NET-ProductRecommendation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nishanc%2FML.NET-ProductRecommendation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nishanc%2FML.NET-ProductRecommendation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nishanc","download_url":"https://codeload.github.com/nishanc/ML.NET-ProductRecommendation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nishanc%2FML.NET-ProductRecommendation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279020642,"owners_count":26086895,"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":"2025-10-14T02:00:06.444Z","response_time":60,"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":["dotnet","microsoft","mldotnet","recommendation-system"],"created_at":"2025-05-12T13:49:44.220Z","updated_at":"2025-10-14T19:08:58.365Z","avatar_url":"https://github.com/nishanc.png","language":"C#","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧠 ML.NET Product Recommendation Demo – Co-Purchase Scenario\n\nThis demo accompanies the tech talk **\"Building Intelligent Applications with ML.NET\"**, where we explore how to use **Matrix Factorization** to build a **product recommendation system** based on co-purchase behavior.\n\n---\n\n## 📌 Scenario\n\nImagine you're running an e-commerce platform. You have customer purchase data and want to recommend products that are **frequently bought together** — even if the user hasn't seen them before.\n\nThis solution uses:\n- ML.NET’s `MatrixFactorizationTrainer`\n- A small sample dataset of `user_id`, `product_id`, and `Label`\n\n---\n\n## 💡 What You'll Learn\n\n- Basics of Matrix Factorization for collaborative filtering\n- How to implement recommendations in ML.NET\n- Training a model using implicit purchase data\n- Making product predictions for a given customer\n\n---\n\n## 🔧 Prerequisites\n\n- [.NET 8 SDK](https://dotnet.microsoft.com/en-us/download)\n- Visual Studio or VS Code\n- ML.NET NuGet Package:\n\n```\ndotnet add package Microsoft.ML\n```\n\n## 🛠️ How to Run the Demo\n\n1. **Clone or download the repo**.\n2. Open the folder in Visual Studio or run via CLI.\n3. Make sure the dataset `amazon.csv` is in the `Data/` folder.\n4. Run the program:\n ```bash\n dotnet run\n ```\n\n \n## 🔗 Further Reading and References\n\n### 📦 ML.NET Samples\n\n- 🔹 [Product Recommendation - Matrix Factorization Problem Sample](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/MatrixFactorization_ProductRecommendation#product-recommendation---matrix-factorization-problem-sample)\n- 🎬 [Movie Recommendation - Matrix Factorization Sample 1 (Program.cs)](https://github.com/dotnet/samples/blob/main/machine-learning/tutorials/MovieRecommendation/Program.cs)\n- 🎬 [Movie Recommendation - Matrix Factorization Sample 2](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/MatrixFactorization_MovieRecommendation#movie-recommendation---matrix-factorization-problem-sample)\n\n### 📘 Documentation and Tutorials\n\n- 📄 [What is ML.NET and How Does It Work?](https://learn.microsoft.com/en-us/dotnet/machine-learning/mldotnet-api)\n- ⚙️ [What is Automated Machine Learning (AutoML)?](https://learn.microsoft.com/en-us/dotnet/machine-learning/automated-machine-learning-mlnet)\n- 🛠️ [ML.NET AutoML Model Builder (Step-by-Step Walkthrough)](https://blog.nishanc.com/2023/08/mlnet-automl-model-builder-step-by-step.html)\n- 🚀 [Deploy a Model in an ASP.NET Core Web API](https://learn.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/serve-model-web-api-ml-net)\n\n### 📊 Data \u0026 Research\n\n- 🛒 [Amazon Sales Dataset (Kaggle)](https://www.kaggle.com/datasets/karkavelrajaj/amazon-sales-dataset?resource=download)\n- 📑 [Machine Learning at Microsoft with ML.NET (Research Paper)](https://arxiv.org/pdf/1905.05715)\n\n### 🤖 Ecosystem \u0026 Showcase\n\n- 🌐 [Open Neural Network Exchange (ONNX)](https://onnx.ai/)\n- 🏢 [Artificial Intelligence \u0026 ML Customer Showcase (Microsoft)](https://dotnet.microsoft.com/en-us/platform/customers/machinelearning-ai)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnishanc%2Fml.net-productrecommendation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnishanc%2Fml.net-productrecommendation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnishanc%2Fml.net-productrecommendation/lists"}