In your code, you just check whether the randomly generated negative sampling exists in the training set, therefore, some negative samples may exist in the test set, Is it reasonable to do so?
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches"
个性化新闻推荐系统，A news recommendation system involving collaborative filtering,content-based recommendation and hot news recommendation, can be adapted easily to be put into use in other circumstances.
A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors for user and item which can be used for ANN search.
Recommender system and evaluation framework for top-n recommendations tasks that respects polarity of feedbacks. Fast, flexible and easy to use. Written in python, boosted by scientific python stack.
tf-recsys contains collaborative filtering (CF) model based on famous SVD and SVD++ algorithm. Both of them are implemented by tensorflow in order to utilize GPU acceleration.
Hi, thanks for this nice package!
AlternatingLeastSquares.recommendhas a parameter calledfilter_items, which, apart from the source code, does not have any documentation.The same parameter is found in
RecommenderBase, [`AnnoyAlter