Abstract
AbstractIn recent years, plenty of researchers work on recommender systems, predicting users’ ratings or preferences for items based on massive data. Traditional statistical learning recommender system includes collaborative filtering (CF), matrix factorization (MF), factorization machine (FM), field-aware factorization machine (FFM), and gradient boosting decision tree (GBDT). Some deep learning algorithms like AutoRec and NeuralCF are introduced in this article. However, it is purposed that existing recommender systems are based on association, and there is a deviation from the actual situation. Thus, inverse propensity score (IPS) and doubly robust model are proposed to debias.KeywordsRecommender systemDeep learningCausal inferenceInverse propensity score
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