gorse: Go Recommender System Engine

Build	Build (AVX2)	Coverage	Document	Report

gorse is a recommender system engine implemented by the Go programming language. It is a package provides components to build a recommender system:

• Data: Load data from built-in datasets or custom files.
• Splitter: Split dataset by k-fold, ratio or leave-one-out.
• Model: Recommendation models based on collaborate filtering including matrix factorization, neighborhood-based method, Slope One and Co-Clustering.
• Evaluator: Implemented RMSE and MAE for rating task. For ranking task, there are Precision, Recall, NDCG, MAP, MRR and AUC.
• Parameter Search: Find best hyper-parameters using grid search or random search.
• Persistence: Save a model or load a model.
• SIMD (Optional): Vectors are computed by AVX2 instructions which are 4 times faster than single instructions in theory.

Installation

go get github.com/zhenghaoz/gorse

Build

If the CPU supports AVX2 and FMA3 instructions, use the avx2 build tag to enable AVX2 support. For example:

go build -tags='avx2' example/benchmark_rating_ml_1m/main.go

Usage

Examples and tutorials could be found in wiki. Let's get started with a simple example:

package main

import (
	"fmt"
	"github.com/zhenghaoz/gorse/base"
	"github.com/zhenghaoz/gorse/core"
	"github.com/zhenghaoz/gorse/model"
)

func main() {
	// Load dataset
	data := core.LoadDataFromBuiltIn("ml-100k")
	// Split dataset
	train, test := core.Split(data, 0.2)
	// Create model
	svd := model.NewSVD(base.Params{
		base.Lr:       0.007,
		base.NEpochs:  100,
		base.NFactors: 80,
		base.Reg:      0.1,
	})
	// Fit model
	svd.Fit(train)
	// Evaluate model
	fmt.Printf("RMSE = %.5f\n", core.RMSE(svd, test, nil))
	// Predict a rating
	fmt.Printf("Predict(4,8) = %.5f\n", svd.Predict(4, 8))
}

The output would be:

RMSE = 0.91305
Predict(4,8) = 4.72873

More examples could be found in the example folder.

Benchmarks

All models are tested by 5-fold cross validation on a PC with Intel(R) Core(TM) i5-4590 CPU (3.30GHz) and 16.0GB RAM. All scores are the best scores achieved by gorse yet.

• Rating Prediction on MovieLens 1M (source)

• Item Ranking on MovieLens 100K (source)

See detail in wiki.

Acknowledge

• CISL@Fudan
• Guibing Guo's librec
• Nicolas Hug's Surprise
• Golang Samples's gopher-vector

References

1. Hug, Nicolas. Surprise, a Python library for recommender systems. http://surpriselib.com, 2017.

2. G. Guo, J. Zhang, Z. Sun and N. Yorke-Smith, LibRec: A Java Library for Recommender Systems, in Posters, Demos, Late-breaking Results and Workshop Proceedings of the 23rd Conference on User Modelling, Adaptation and Personalization (UMAP), 2015.

3. Luo, Xin, et al. "An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems." IEEE Transactions on Industrial Informatics 10.2 (2014): 1273-1284.

4. Lemire, Daniel, and Anna Maclachlan. "Slope one predictors for online rating-based collaborative filtering." Proceedings of the 2005 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2005.

5. George, Thomas, and Srujana Merugu. "A scalable collaborative filtering framework based on co-clustering." Data Mining, Fifth IEEE international conference on. IEEE, 2005.

6. Guo, Guibing, Jie Zhang, and Neil Yorke-Smith. "A Novel Bayesian Similarity Measure for Recommender Systems." IJCAI. 2013.

7. Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback datasets." Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 2008.

8. Massa, Paolo, and Paolo Avesani. "Trust-aware recommender systems." Proceedings of the 2007 ACM conference on Recommender systems. ACM, 2007.

9. Desrosiers, Christian, and George Karypis. "A comprehensive survey of neighborhood-based recommendation methods." Recommender systems handbook. Springer, Boston, MA, 2011. 107-144.

10. Koren, Yehuda. "Factorization meets the neighborhood: a multifaceted collaborative filtering model." Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008.