Data science and artificial intelligence for responsible recommendations

With the advancement of data science and AI, more and more powerful and accurate recommender systems (RSs) have been developed. They provide recommendation services in various areas, including shopping, eating, travelling and entertainment. RSs have achieved a great success and benefted the society. However, most of the research on RS has focused on the improvement of the recommendation accuracy, while ignoring other important qualities, such as trustworthiness (robustness, fairness, explainability, privacy and security) and social impact (influence on users' recognition and behaviours) of the recommendations. These are important aspects and cannot be overlooked since they measure properties that determine whether the recommendation service is reliable, trustworthy and benefcial to individual users and society. In this work, responsible recommendations refer to trustworthy recommendation techniques and positive-social-impact recommendation results.

This workshop aims to engage with active researchers from the RS community, and other communities, as social science, to discuss state-of-the-art research results related to the core challenges of responsible recommendation services. We will focus on two main topics of responsible RSs: (1) developing reliable and trustworthy RS models and algorithms, to provide reliable recommendation results when facing a complex, uncertain and dynamic scenario; (2) assessing the social influence of RSs on human's recognition and behaviours and ensuring the influence is positive to the society.