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.

翻译：建议系统(RSs)在现代社会中普遍存在,是人类和AI之间互动的最大点之一。现代RSs往往使用深层次的学习模式来实施,这些模式很难解释,在建议设想中,这个问题特别令人发指,因为它侵蚀了用户对RS的信任。相比之下,新引进的Tsetlin机器(TM)由于其内在的可解释性而具有一些宝贵的特性。TMs作为一个技术仍然相当年轻。由于以前没有为TMs开发任何RS,因此有必要对这种系统的实际性进行一些初步研究。在本文件中,我们根据TMs开发了第一个RSs,以评价其在这个应用领域的实用性。本文将TMs的可行性与在RS领域流行的其他机器学习模式加以比较。我们用香草喂养的深层学习模式来培训和调查TM的性能。这些比较基于模型的性能、可解释性/可解释性和可调适性。此外,我们为与RS有关的类似机器学习解决方案提供了一些基准性业绩比较。