  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.
