论文标题
在推荐系统的背景下，Tsetlin机器与深神经网络之间的比较
A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems
论文作者
论文摘要
推荐系统（RSS）在现代社会中无处不在，并且是人类与人工智能之间最大的互动点之一。现代RSS通常是使用深度学习模型来实施的，这些模型臭名昭著地解释。在推荐方案的背景下，此问题尤其激怒，因为它侵蚀了用户对RS的信任。相比之下，新引入的Tsetlin机器（TM）由于其固有的解释性而具有一些有价值的特性。 TMS仍然很年轻，就像一项技术一样。由于以前没有针对TMS开发RS，因此有必要就此类系统的实用性进行一些初步研究。在本文中，我们开发了基于TMS的第一个RS，以评估其在该应用领域的实用性。本文将TMS与Rs领域中普遍存在的其他机器学习模型进行比较。与香草进料深度学习模型相比，我们训练和研究TM的性能。这些比较基于模型性能，可解释性/解释性和可伸缩性。此外，我们与与RSS相关的类似机器学习解决方案提供了一些基准性能比较。
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.
