A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems
This work addresses the need for interpretable AI in recommendation systems to enhance user trust, but it is incremental as it applies an existing method (TMs) to a new domain.
The paper tackles the problem of interpretability in recommendation systems by developing the first Tsetlin Machine-based system and comparing it to deep neural networks, finding that TMs offer competitive performance with improved interpretability, though specific numbers are not provided.
Recommendation Systems (RSs) are ubiquitous in modern society and are one of the largest points of interaction between humans and AI. Modern RSs are often implemented using deep learning models, which are infamously difficult to interpret. This problem is particularly exasperated in the context of recommendation scenarios, as it erodes the user's trust in the RS. In contrast, the newly introduced Tsetlin Machines (TM) possess some valuable properties due to their inherent interpretability. TMs are still fairly young as a technology. As no RS has been developed for TMs before, it has become necessary to perform some preliminary research regarding the practicality of such a system. In this paper, we develop the first RS based on TMs to evaluate its practicality in this application domain. This paper compares the viability of TMs with other machine learning models prevalent in the field of RS. We train and investigate the performance of the TM compared with a vanilla feed-forward deep learning model. These comparisons are based on model performance, interpretability/explainability, and scalability. Further, we provide some benchmark performance comparisons to similar machine learning solutions relevant to RSs.