| Abstract: | The market basket data in the form of a binary user–item matrix or a binary item–user matrix can be modeled as a binary classification problem, which actually tackles collaborative filtering (CF) as well as target marketing. Effective variable selection (VS) can increase the prediction accuracy as well as identify important users or items in CF as well as target marketing. Therefore, we propose two new VS approaches: a Pearson correlation‐based approach and a forward random forests regression‐based approach, comparing the performance in a variety of experimental settings. The experimental results show that the proposed VS approaches outperform the conventional approaches in the examples. Furthermore, the experimental results are more reasonable and informative than the previous experimental results because the binary misclassification error and Top‐N accuracy for the user CF, the item CF, the user modeling, and the item modeling are all considered in this paper. |
