Towards Trustworthy Recommender Systems: From Shallow Models to Deep Models to Large Models
16Research Talk
Yongfeng Zhang, Rutgers University
As the bridge between humans and AI, recommender system is at the frontier of Human-centered AI research. However, inappropriate use or development of recommendation techniques may bring negative effects to humans and the society at large, such as user distrust due to the non-transparency of the recommendation mechanism, unfairness of the recommendation algorithm, user uncontrollability of the recommendation system, as well as user privacy risks due to the extensive use of users’ private data for personalization. In this talk, we will discuss how to build trustworthy recommender systems along the progress that recommendation algorithms advance from shallow models to deep models to large models, including but not limited to the unique role of recommender system research in the AI community as a representative Subjective AI task, the relationship between Subjective AI and trustworthy computing, as well as typical recommendation methods on different perspectives of trustworthy computing, such as causal and counterfactual reasoning, neural-symbolic modeling, natural language explanations, federated learning, user controllable recommendation, echo chamber mitigation, personalized prompt learning, and beyond.
Learn more about the Responsible AI Workshop: https://www.microsoft.com/en-us/research/event/responsible-ai-an-interdisciplinary-approach-workshop/
This workshop was part of the Microsoft Research Summit 2022: https://www.microsoft.com/en-us/research/event/microsoft-research-summit-2022/