Explainability in Music Recommender Systems
Recent years have seen an upsurge of interest in explainable recommendations, even though the concept already emerged in the 2000s ( schafer1999recommender). This evolution of explainable RSs has been accompanied by an increasing popularity of eXplainable Artificial Intelligence ( XAI), with which it shares roots, approaches, and terminology. XAI represents the convergence of many research disciplines, including computer science, human-computer-interaction, philosophy, and psychology. Coherent and stable XAI definitions and terms have started to appear only recently ( arrieta2020explainable; guidotti2018survey; lipton2018mythos). Meanwhile, RSs research has developed explanation-related concepts that are unknown to general XAI
RSs themselves, which differ w. r. t. their tasks, inputs, and results from general trends in XAI . Linking these two explainability realms would not only result in a more standardized approach to explanations in RSs but also in a direct application of methods from XAI to MRSs
In this section, we review definitions and concepts of explainability in RSs. Subsequently, we compare and connect them with the ones of XAI. Note that this is not a survey of XAI or explainable RSs as other valuable resources exist on this matter ( DBLP:journals/ftir/ZhangC20; arrieta2020explainable; guidotti2018survey; nunes2017systematic; Survey2007; gilpin2018explaining).
What does it mean to explain a recommendation? Within the field, Tintarev et al. addresses this question with ”to make clear by giving a detailed description”, and Zhang et al. with ”an explainable recommendation aims to answer the question of why”. We can thus discern a role of explanations as complementary information to the recommendation. But these definitions are limited; for instance, ensuring fair recommendations involves tracing the ”why” of a recommendation, but only regarding certain critical aspects (e. g., potential gender biases) and it does not tell how to act upon them. As we develop next, ”complementary information” and ”fair recommendations” shape two of the many facets of explainability.
Borrowing general ideas from recent harmonization efforts of terms, it is more convenient to distinguish between explanation objects and goals. In particular, explanations designate the result of an explanation system, they form an ”interface between the system to explain and a target audience” . Quite interchangeably with explainability, we will use the term interpretability , with a more passive characteristic: a system can be interpretable – e. g., decision trees are often interpretable, neural networks are not. The opposite notion is often referred to as . We stress that automatically concluding that trees and linear regressions are interpretable and that neural networks are not is questionable. As we will see next, this depends on a precise formulation of explanation tasks that do not admit one-size-fits-all rules.
The previous mention of ”audience” is essential, since a given explanation type may only convey meaningful information to specific people. In research, the target audience of explanations is usually end-users as they are the targets of the recommendation decision and could be skeptical about it. Nevertheless, other stakeholders may be interested in receiving explanations, e. g., system designers and data scientists may inquire wherever their system bases its decisions on discriminatory biases from the data.
