ensemble-based top-k recommender system considering incomplete data

Recommender systems have been widely used in e- commerce applications. they are a sub-class of information filtering system used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be of user-interest (top-k recommendation problem). demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two significant challenges in recommender systems. however, the latter is far from satisfactory because human decisions are affected by environmental conditions, and they might change over time. in this paper, we introduce an innovative method to impute ratings to missed components of the rating matrix. we also design an ensemble-based method to obtain top-k recommendations. in order to evaluate the performance of the proposed method, several experiments have been conducted based on 10-fold cross-validation over real-world datasets. the experimental results show that the proposed method is superior to the state-of-the-art competing methods regarding the applied evaluation metrics.