Models

There are all models implemented in gorse:

Model Data Task Multi-threading Fit
  explicit implicit weight rating ranking
BaseLine      
NMF [3]      
SVD      
SVD++ [8]    
KNN [7]    
CoClustering [5]    
SlopeOne [4]    
ItemPop      
KNN (Implicit) [6]  
WRMF [6]    
BPR [9]      

Apparently, these models using implicit feedbacks are more general since explicit feedbacks could be converted to implicit feedbacks and item ranking could be done by rating prediction.

References

[1]Hug, Nicolas. Surprise, a Python library for recommender systems. http://surpriselib.com, 2017.
[2]
  1. 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]“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](1, 2) 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.
[7]Desrosiers, Christian, and George Karypis. “A comprehensive survey of neighborhood-based recommendation methods.” Recommender systems handbook. Springer, Boston, MA, 2011. 107-144.
[8]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.
[9]Rendle, Steffen, et al. “BPR: Bayesian personalized ranking from implicit feedback.” Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, 2009.

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