This solution enables you to create product recommendations predictive models based on historical transaction data and information on the product catalog. Item-to-Item Recommendations. This is the "Customers who liked this product also liked these other products" scenario. Increase the discoverability of items in your catalog by showing relevant products to your customers. Personalized Recommendations. By providing the recent history of transactions for a given user, the SAR algorithm can return personalized recommendations for that user. Train models using the SAR (Smart Adaptive Recommendations) algorithm. Request a previously created model for recommendations. 

An example cross-platform movie recommendation application built using Xamarin.Forms and Azure ML service

Recommenders Engine Example Layout (REEL) is a cross-platform movie recommendation application that demonstrates the integration of example recommendation algorithms from Microsoft/Recommenders into an application workflow. The following sections will demonstrate how to locally build REEL. These sections will guide the reader into:. Currently, REEL runs Simple Algorithm for Recommendation (SAR) and LightGBM algorithms. The application is built using Xamarin.Forms, so it is supported on iOS, Android and Windows Desktop. The algorithms are trained on the MovieLens dataset. Creating an ML model endpoint Setting up and populating Azure SQL Database Setting up Azure Search Running the app's backend on Azure using AKS Installing the Xamarin.Forms app for the mobile client 

NReco Recommender is a .NET port of Apache Mahout CF java engine (standalone, non-Hadoop version)

NReco Recommender is a .NET port of Apache Mahout Collaborative Filtering engine "Taste" (standalone, non-Hadoop version). Names of namespaces, class names and public methods are preserved but aligned with C# naming conventions. 

Easy to use (but high performance!) weighted selection implementation, in C#.

WeightedSelector.NET is a .NET Standard 2 project (which means it should work most anywhere) that lets you assign weights to a set of choices, then make decisions based on each choice's proportion of the total weight. This is useful for scenarios where choices are made based on complicated but quantifiable factors, or where you need to choose between a number of reasonable choices in a way that appears semi-random. Great examples of the latter case include suggestion engines and game AI. 

Recommendation engine using collaborative filtering and matrix factorization

A sample web application for .NET developers that uses AWS, .NET Core, Amazon Personalize, and Amazon S3 to show how you can quickly and easily build a Movie Recommendations Engine.

This walkthrough outlines the steps to build a movie recommendations engine web application using Amazon Personalize, ASP.NET MVC and .NET Core Framework. 

Kentico Smart Kontent Recommendations .NET SDK

Forget manually browsing, updating and testing your related content - create personalized content recommendations using our integrated AI-based recommendation engine fast and simple. 

LOLTFT realtime recommendation engine (WIP)