Models

There are all models implemented in gorse:

Model Data Task Multi-threading 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.

Non-Personalized Models

ItemPop

The only non-personalized 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]
  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.