Xiting Wang
Senior Researcher
About
Xiting Wang is now a senior researcher at Microsoft Research Asia. Her research interests include explainable recommendation, text mining and visual text analytics. She has published academic papers on reputable international conferences and journals in her research area, such as KDD, TKDE, AAAI, IJCAI, TVCG and VAST. One of her first author papers has been chosen as the TVCG spotlight article for Dec. 2016. She is a senior program committee member of AAAI and is a program committee member of many top conferences.
Xiting Wang received her Ph.D. degree in Computer Science from Tsinghua University in 2017, and a B.S. degree in Electronics Engineering from Tsinghua University.
Research activities: Senior Program Committee of AAAI Conference on Artificial Intelligence (AAAI), 2021 Program Committee of the WEB Conference (WebConf), 2020 Program Committee of International Joint Conferences on Artificial Intelligence (IJCAI), 2020 Program Committee of AAAI Conference on Artificial Intelligence (AAAI), 2020 Program Committee…
Featured
Explainable Recommendation: Perspectives from Knowledge Graph Reasoning and Natural Language Generation (Keynote for the EARS Workshop of SIGIR 2020)
I gave a keynote speech for the SIGIR 2020 workshop on Explainable Recommendation and Search. The deck can be downloaded here: https://www.microsoft.com/en-us/research/uploads/prod/2020/07/SIGIR_EARS_2020_Reasoning_Generation.pptx
Towards A Deep and Unified Understanding of Deep Neural Models in NLP
We define a unified information-based measure to provide quantitative explanations on how intermediate layers of deep Natural Language Processing (NLP) models leverage information of input words. Our method advances existing explanation methods by addressing issues in coherency and generality. Explanations generated by using our method are consistent and faithful across different timestamps, layers, and models. We show how our method can be applied to four widely used models in NLP and explain their performances on three real-world benchmark datasets.
Explainable Recommendation Through Attentive Multi-View Learning
Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental…
TopicPanorama: A Full Picture of Relevant Topics (TVCG Spotlight Article for Dec. 2016)
Xiting Wang, Shixia Liu, Junlin Liu, Jianfei Chen, Jun Zhu, Baining Guo. This paper presents a visual analytics approach to analyzing a full picture of relevant topics discussed in multiple sources, such as news, blogs, or micro-blogs. The full picture consists of a number of common topics covered by multiple sources, as…