Z
Zi Yang
Researcher at Tsinghua University
Publications - 24
Citations - 2459
Zi Yang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Heterogeneous network & Ranking (information retrieval). The author has an hindex of 16, co-authored 24 publications receiving 1995 citations. Previous affiliations of Zi Yang include Carnegie Mellon University.
Papers
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Proceedings ArticleDOI
Social influence analysis in large-scale networks
TL;DR: Topical Affinity Propagation (TAP) is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework and can take results of any topic modeling and the existing network structure to perform topic-level influence propagation.
Posted Content
Towards a Human-like Open-Domain Chatbot
Daniel Adiwardana,Minh-Thang Luong,David R. So,Jamie Hall,Noah Fiedel,Romal Thoppilan,Zi Yang,Apoorv Kulshreshtha,Gaurav Nemade,Yifeng Lu,Quoc V. Le +10 more
TL;DR: Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations, is presented and a human evaluation metric called Sensibleness and Specificity Average (SSA) is proposed, which captures key elements of a human-like multi- turn conversation.
Proceedings ArticleDOI
Understanding retweeting behaviors in social networks
TL;DR: This paper proposes a factor graph model to predict users' retweeting behaviors and shows that this method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors.
Proceedings ArticleDOI
Social context summarization
TL;DR: A dual wing factor graph (DWFG) model is proposed, which utilizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries, and an efficient algorithm is designed to learn the proposed factor graph model.
Journal ArticleDOI
Topic level expertise search over heterogeneous networks
TL;DR: This paper proposes a topic level random walk method for ranking the different objects in the academic network, and develops a topical graph search function, based on the topic modeling and citation tracing analysis.