Algorithm for generating negative cases for collaborative filtering recommender

Abstract

AbstractMost collaborative filtering recommenders based on deep learning utilize implicit feedback information such as likes and bookmarks to infer user preferences rather than explicit feedback such as ratings provided by users. However, collaborative filtering models utilizing implicit feedback pose a chronic problem, that is, the “one‐class” problem (also known as the one‐class collaborative filtering problem), which causes trivial solutions due to lack of negative examples. A one‐class problem free‐boosting (OCF‐B) algorithm is proposed in this study to solve the one‐class problem by considering a user's preference pattern when generating negative cases and training the model with both positive and negative cases. More specifically, the OCF‐B algorithm iteratively selects negative cases that exacerbate the loss value of the object function and replace them with other negative cases when extracting negative cases from unknown cells of the user–item interaction matrix. In an experiment using four datasets, the results show that negative cases selected meticulously via the OCF‐B algorithm improve the prediction performance not only for negative cases, but also for positive cases. In addition, the prediction performance was better than that of the existing zero‐injection method.