42

GitHub - ZhangZhenghao/gorse: Go Recommender System Engine

 5 years ago
source link: https://github.com/ZhangZhenghao/gorse
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.

README.md

gorse: Go Recommender System Engine

Build Status codecov Document Go Report Card

gorse is a recommender system engine implemented by the go programming language provide

  • Algorithm: Predict ratings based on collaborate filtering. Including matrix factorization and neighborhood-based method.
  • Data: Load data from the built-in dataset or file. Split data to train set and test set.
  • Evaluator: Evaluate models by cross-validation using RMSE or MAE.

Installation

go get -t -v -u github.com/ZhangZhenghao/gorse/core

Usage

// Load build-in data
data := core.LoadDataFromBuiltIn("ml-100k")
// Create a recommender
algo := core.NewSVD(nil)
// Cross validate
cv := core.CrossValidate(algo, data, []core.Evaluator{core.RMSE, core.MAE},5, 0, nil)
// Print RMSE & MAE
fmt.Printf("RMSE = %f, MAE = %f\n", 
           stat.Mean(cv[0].Tests, nil), 
           stat.Mean(cv[1].Tests, nil))

Output:

RMSE = 0.938904, MAE = 0.737349

Tutorial

Benchmarks

All algorithms are tested on a PC with Intel(R) Core(TM) i5-4590 CPU (3.30GHz) and 16.0GB RAM. RMSE scores and MAE scores are used to check the correctness comparing to other implementation but not the best performance. Parameters are set as default values and identical to other implementation.

Movielens 100K RMSE MAE Time RMSE[REF] MAE[REF] Random 1.518610 1.218645 0:00:01 1.514[1] 1.215[1] BaseLine 0.943741 0.741738 0:00:01 0.944[1] 0.748[1] SVD 0.938904 0.737349 0:00:05 0.934[1] 0.737[1] SVD++ 0.922710 0.721740 0:03:27 0.92[1] 0.722[1] NMF[3] 0.970431 0.762025 0:00:07 0.963[1] 0.758[1] KNN 0.978720 0.773133 0:00:03 0.98[1] 0.774[1] Centered k-NN 0.952928 0.751693 0:00:03 0.951[1] 0.749[1] k-NN Z-Score 0.953098 0.748464 0:00:03

k-NN Baseline 0.933512 0.734706 0:00:04 0.931[1] 0.733[1] Slope One[4] 0.940748 0.741195 0:00:02 0.946[1] 0.743[1] Co-Clustering[5] 0.968219 0.760593 0:00:01 0.963[1] 0.753[1] Movielens 1M RMSE MAE Time RMSE[REF] MAE[REF] Random 1.506756 1.208171 0:00:01 1.504[1] 1.206[1] BaseLine 0.909781 0.717029 0:00:09 0.909[1] 0.719[1] SVD 0.877262 0.688397 0:01:07 0.873[1] 0.686[1] SVD++ 0.865424 0.677274 0:59:08 0.862[1] 0.673[1] NMF[3] 0.917979 0.726059 0:01:33 0.916[1] 0.724[1] KNN 0.922540 0.727227 0:03:15 0.923[1] 0.727[1] Centered k-NN 0.929107 0.738920 0:03:39 0.929[1] 0.738[1] k-NN Z-Score 0.930754 0.737239 0:03:38

k-NN Baseline 0.896017 0.707469 0:03:36 0.895[1] 0.706[1] Slope One[4] 0.908397 0.717344 0:00:54 0.907[1] 0.715[1] Co-Clustering[5] 0.916752 0.720052 0:00:10 0.915[1] 0.717[1]

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. Li, Dongsheng, et al. "Mixture-Rank Matrix Approximation for Collaborative Filtering." Advances in Neural Information Processing Systems. 2017.


report

Recommend

About Joyk


Aggregate valuable and interesting links.
Joyk means Joy of geeK