EARS 2019
International Workshop on ExplainAble Recommendation and Search

Workshop website: https://ears2019.github.io/

The motivation of the workshop is to promote the research and application of Explainable Recommendation and Search, under the background of Explainable AI in a more general sense. Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, which can help improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc.

In a broader sense, researchers in the whole artificial intelligence community have also realized the importance of Explainable AI, which aims to address a wide range of AI explainability problems in deep learning, computer vision, automatic driving systems, and natural language processing tasks. Recent AI regulations such as EU GDPR and California Consumer Privacy Act of 2018 also encourage the explainability and users’ right to explanation of algorithmic decisions in AI systems. As an important branch of AI research, it highlights the importance our IR/RecSys community to address the explainability issues of various recommendation and search systems.

We welcome contributions of both long and short papers from a wide range of topics, including but not limited to explainable recommendation and search models, incorporating multi-modal information for explanation, evaluation of explainable recommendation and search, user study for explainable recommendation and search, etc. More topics are listed in the call for papers.

Organizers

Yongfeng Zhang

Rutgers University

USA

Yi Zhang

University of California, Santa Cruz

USA

Min Zhang

Tsinghua University

China

Chirag Shah

Important Dates