A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem

Paper recommendation systems are important for alleviating academic information overload. Such systems provide personalized recommendations based on implicit feedback from users, supplemented by their subject information, citation networks, etc. However, such recommender systems face problems like data sparsity for positive samples and uncertainty for negative samples. In this paper, we address these two issues and improve upon them from the perspective of metric learning. The algorithm is modeled as a push-pull loss function. For the positive sample pull-out operation, we introduce a context factor, which accelerates the convergence of the objective function through the multiplication rule to alleviate the data sparsity problem. For the negative sample push operation, we adopt an unbiased global negative sample method and use an intermediate matrix caching method to greatly reduce the computational complexity. Experimental results on two real datasets show that our method outperforms other baseline methods in terms of recommendation accuracy and computational efficiency. Moreover, our metric learning method that introduces context improves by more than 5% over the element-wise alternating least squares method. We demonstrate the potential of metric learning in addressing the problem of implicit feedback recommender systems with positive and negative sample imbalances.