Trustworthy Recommender Systems

TL;DR

This paper reviews the emerging field of trustworthy recommender systems, emphasizing the importance of transparency, fairness, and robustness alongside accuracy, and proposes a conceptual framework for their development.

Contribution

It offers a systematic overview of trustworthy recommender systems, discusses key challenges, and introduces a novel framework to guide future research in this area.

Findings

Highlights the shift from accuracy to trustworthiness in RS research

Identifies key challenges in building TRSs, including bias and robustness

Proposes a conceptual framework to support TRS development

Abstract

Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. As a result, it has become clear that a strict focus on RS accuracy is limited and the research must consider other important factors, e.g., trustworthiness. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.