DiscoRec 0.1.0
dotnet add package DiscoRec --version 0.1.0
NuGet\Install-Package DiscoRec -Version 0.1.0
<PackageReference Include="DiscoRec" Version="0.1.0" />
paket add DiscoRec --version 0.1.0
#r "nuget: DiscoRec, 0.1.0"
// Install DiscoRec as a Cake Addin #addin nuget:?package=DiscoRec&version=0.1.0 // Install DiscoRec as a Cake Tool #tool nuget:?package=DiscoRec&version=0.1.0
Disco.NET
Recommendations for .NET using collaborative filtering
- Supports user-based and item-based recommendations
- Works with explicit and implicit feedback
- Uses high-performance matrix factorization
Installation
Run
dotnet add package DiscoRec
Getting Started
Import the library
using DiscoRec;
Prep your data in the format userId, itemId, value
var data = new Dataset<string, string>();
data.Add("user_a", "item_a", 5.0f);
data.Add("user_a", "item_b", 3.5f);
data.Add("user_b", "item_a", 4.0f);
IDs can be integers or strings
data.Add(1, "item_a", 5.0f);
If users rate items directly, this is known as explicit feedback. Fit the recommender with:
var recommender = Recommender<string, string>.FitExplicit(data);
If users don’t rate items directly (for instance, they’re purchasing items or reading posts), this is known as implicit feedback. Leave out the rating.
var data = new Dataset<string, string>();
data.Add("user_a", "item_a");
data.Add("user_a", "item_b");
data.Add("user_b", "item_a");
var recommender = Recommender<string, string>.FitImplicit(data);
Each
userId
/itemId
combination should only appear once
Get user-based recommendations - “users like you also liked”
recommender.UserRecs(userId, 5);
Get item-based recommendations - “users who liked this item also liked”
recommender.ItemRecs(itemId, 5);
Get predicted ratings for a specific user and item
recommender.Predict(userId, itemId);
Get similar users
recommender.SimilarUsers(userId, 5);
Examples
MovieLens
Load the data
var data = await Data.LoadMovieLens();
Create a recommender
var recommender = Recommender<int, string>.FitExplicit(data, new RecommenderOptions { Factors = 20 });
Get similar movies
var recs = recommender.ItemRecs("Star Wars (1977)");
foreach (var rec in recs)
Console.WriteLine("{0}: {1}", rec.Id, rec.Score);
Storing Recommendations
Save recommendations to your database.
Alternatively, you can store only the factors and use a library like pgvector-dotnet. See an example.
Algorithms
Disco uses high-performance matrix factorization.
- For explicit feedback, it uses stochastic gradient descent
- For implicit feedback, it uses coordinate descent
Specify the number of factors and iterations
var recommender = Recommender<string, string>.FitExplicit(data, new RecommenderOptions { Factors = 8, Iterations = 20 });
Validation
Pass a validation set
var recommender = Recommender<string, string>.FitExplicit(trainSet, validSet);
Cold Start
Collaborative filtering suffers from the cold start problem. It’s unable to make good recommendations without data on a user or item, which is problematic for new users and items.
recommender.UserRecs(newUserId, 5);
There are a number of ways to deal with this, but here are some common ones:
- For user-based recommendations, show new users the most popular items
- For item-based recommendations, make content-based recommendations
Reference
Get ids
recommender.UserIds();
recommender.ItemIds();
Get the global mean
recommender.GlobalMean();
Get factors
recommender.UserFactors(userId);
recommender.ItemFactors(itemId);
History
View the changelog
Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/ankane/disco-dotnet.git
cd disco-dotnet
dotnet test
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net6.0 is compatible. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
-
net6.0
- Microsoft.ML (>= 3.0.1)
- Microsoft.ML.Recommender (>= 0.21.1)
NuGet packages
This package is not used by any NuGet packages.
GitHub repositories (1)
Showing the top 1 popular GitHub repositories that depend on DiscoRec:
Repository | Stars |
---|---|
pgvector/pgvector-dotnet
pgvector support for .NET (C#, F#, and Visual Basic)
|
Version | Downloads | Last updated |
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0.1.0 | 112 | 7/21/2024 |