Less is more: improving neural-based collaborative filtering by using landmark modeling

Collaborative Filtering (CF) has been extensively studied over the last decade. Among the most successful methods, neural-based methods have changed the research landscape by bringing cutting-edge artificial neural networks (ANN) and big data to offer the best personalization users have ever experienced. However, a significant issue arose from these neural-based methods is that the increase of the model complexity directly impacts the computational cost. In general, this drawback is a consequence of the high dimensionality and sparsity of the input feature space. We believe that reducing sparsity and dimensionality of the input features is essencial to enhance accuracy while keeping computational costs low. This paper investigates an alternative modeling for the CF setting to represent users or items by using landmarks. This modeling drastically decreases the input feature space and eliminate sparsity while maintaining the underlying information needed to achieve great accuracy. Based on that, we propose a novel neural-based CF method via landmark modeling. Experiments on six real-world benchmark CF datasets were conducted by comparing the proposed method to well-known and widely-used CF methods of the state-of-the-art. The results show that the Neural Landmark method outperforms the other methods in both accuracy and computational performance. (C) 2022 Elsevier Inc. All rights reserved.