sbr-go

A recommender system package for Go.

Sbr implements state-of-the-art sequence-based models, using the history of what a user has liked to suggest new items. As a result, it makes accurate prediction that can be updated in real-time in response to user actions without model re-training.

Usage

You can fit a model on the Movielens 100K dataset in about 10 seconds using the following code:

	// Load the data.
	data, err := sbr.GetMovielens()
	if err != nil {
		panic(err)
	}
	fmt.Printf("Loaded movielens data: %v users and %v items for a total of %v interactions\n",
		data.NumUsers(), data.NumItems(), data.Len())

	// Split into test and train.
	rng := rand.New(rand.NewSource(42))
	train, test := sbr.TrainTestSplit(data, 0.2, rng)
	fmt.Printf("Train len %v, test len %v\n", train.Len(), test.Len())

	// Instantiate the model.
	model := sbr.NewImplicitLSTMModel(train.NumItems())

	// Set the hyperparameters.
	model.ItemEmbeddingDim = 32
	model.LearningRate = 0.16
	model.L2Penalty = 0.0004
	model.NumEpochs = 10
	model.NumThreads = 1

	// Set random seed
	var randomSeed [16]byte
	for idx := range randomSeed {
		randomSeed[idx] = 42
	}
	model.RandomSeed = randomSeed

	// Fit the model.
	fmt.Printf("Fitting the model...\n")
	loss, err := model.Fit(&train)
	if err != nil {
		panic(err)
	}

	// And evaluate.
	fmt.Printf("Evaluating the model...\n")
	mrr, err := model.MRRScore(&test)
	if err != nil {
		panic(err)
	}
	fmt.Printf("Loss %v, MRR: %v\n", loss, mrr)

Installation

Run

go get github.com/maciejkula/sbr-go

followed by

make

in the installation directory. This wil download the package's native dependencies. On both OSX and Linux, the resulting binaries are fully statically linked, and you can deploy them like any other Go binary.

If you prefer to build the dependencies from source, run make source instead.