Abstract
Data sparsity is a well-known historical limitation of recommender systems that still impacts the performance of state-of-the-art approaches. One practical technique to mitigate this issue involves transferring information from other domains or tasks to compensate for scarcity in the target domain, where the recommendations must be performed. Following this idea, we propose a novel approach based on Neuro-Symbolic computing designed for the knowledge transfer task in recommender systems. In particular, we use a Logic Tensor Network (LTN) to train a vanilla Matrix Factorization (MF) model for rating prediction. We show how the LTN can be used to regularize the MF model using axiomatic knowledge that permits injecting pre-trained information learned by Collaborative Filtering on a different task or domain. Extensive experiments comparing our model with a baseline MF on two versions of a novel real-world dataset prove our proposal’s potential in the knowledge transfer task. In particular, our model consistently outperforms the MF, suggesting that the knowledge is effectively transferred to the target domain via logical reasoning. Moreover, an experiment that drastically decreases the density of user-item ratings shows that the benefits of the acquired knowledge increase with the sparsity of the dataset, showing the importance of exploiting knowledge from a denser source of information when training data is scarce in the target domain.