Overcoming Recommendation Limitations with Neuro-Symbolic Integration

Abstract

Despite being studied for over twenty years, Recommender Systems (RSs) still suffer from important issues that limit their applicability in real-world scenarios. Data sparsity, cold start, and explainability are some of the most impacting problems. Intuitively, these historical limitations can be mitigated by injecting prior knowledge into recommendation models. Neuro-Symbolic (NeSy) approaches are suitable candidates for achieving this goal. Specifically, they aim to integrate learning (e.g., neural networks) with symbolic reasoning (e.g., logical reasoning). Generally, the integration lets a neural model interact with a logical knowledge base, enabling reasoning capabilities. In particular, NeSy approaches have been shown to deal well with poor training data, and their symbolic component could enhance model transparency. This gives insights that NeSy systems could potentially mitigate the aforementioned RSs limitations. However, the application of such systems to RSs is still in its early stages, and most of the proposed architectures do not really exploit the advantages of a NeSy approach. To this end, we conducted preliminary experiments with a Logic Tensor Network (LTN), a novel NeSy framework. We used the LTN to train a vanilla Matrix Factorization model using a First-Order Logic knowledge base as an objective. In particular, we encoded facts to enable the regularization of the latent factors using content information, obtaining promising results. In this paper, we review existing NeSy recommenders, argue about their limitations, show our preliminary results with the LTN, and propose interesting future works in this novel research area. In particular, we show how the LTN can be intuitively used to regularize models, perform cross-domain recommendation, ensemble learning, and explainable recommendation, reduce popularity bias, and easily define the loss function of a model.