Models¶
There are all models implemented in gorse:
Model  Data  Task  Multithreading Fit  

explicit  implicit  weight  rating  ranking  
BaseLine  Yes  Yes  Yes  No  
NMF [3]  Yes  Yes  Yes  No  
SVD  Yes  Yes  Yes  No  
SVD++ [8]  Yes  Yes  Yes  Yes  
KNN [7]  Yes  Yes  Yes  Yes  
CoClustering [5]  Yes  Yes  Yes  Yes  
SlopeOne [4]  Yes  Yes  Yes  Yes  
ItemPop  Yes  Yes  Yes  No  
KNN (Implicit) [6]  Yes  Yes  Yes  Yes  
WRMF [6]  Yes  Yes  Yes  Yes  No  
BPR [9]  Yes  Yes  Yes  No 
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.
NonPersonalized Models¶
ItemPop¶
The only nonpersonalized model is ItemPop. It always recommmends top K popular items to all users.
Personalized Models¶
References¶
[1]  Hug, Nicolas. Surprise, a Python library for recommender systems. http://surpriselib.com, 2017. 
[2] 

[3]  Luo, Xin, et al. “An efficient nonnegative matrixfactorizationbased approach to collaborative filtering for recommender systems.” IEEE Transactions on Industrial Informatics 10.2 (2014): 12731284. 
[4]  “Slope one predictors for online ratingbased 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 coclustering.” 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 neighborhoodbased recommendation methods.” Recommender systems handbook. Springer, Boston, MA, 2011. 107144. 
[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 twentyfifth conference on uncertainty in artificial intelligence. AUAI Press, 2009. 