A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems

TL;DR

This paper evaluates the practicality of Tsetlin Machines in recommendation systems, comparing their performance, interpretability, and scalability against deep neural networks and other models.

Contribution

It introduces the first recommendation system based on Tsetlin Machines and provides a comparative analysis with deep learning models in this context.

Findings

Tsetlin Machines show promising interpretability advantages.

Performance of Tsetlin Machines is comparable to deep learning models.

Scalability of Tsetlin Machines is assessed against existing models.

Abstract

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.