Improving the Accuracy of Sequential Recommendation Using a Time-aware Architecture

Abstract

Explainable artificial intelligence (XAI) is a new field within artificial intelligence (AI) and machine learning (ML). XAI offers transparency of AI and MLthat can bridge the gap in information that has been absent from "black-box" models. Given its nascency, there are several taxonomies of XAI in the literature. The current paper incorporates the taxonomies in the literature into one unifying framework, which defines the types of explanations, types of transparency, and model methods that together inform the user's processes towards developing trust in AI and ML systems.