演讲人：University of Nottingham Ke (Adam) Zhou

讲座时间：1月6日（周五）15:00

讲座地点：信息楼四楼学术报告厅

Ke (Adam) Zhou is an assistant professor of data science at University of Nottingham, School of Computer Science. His research interests and expertise lie in web search and analytics, evaluation metrics, text mining and human computer interaction. He has published in reputable conferences and journals (SIGIR, WWW, WSDM, TOIS, PLOS ONE), and won the best paper award in ECIR'15 and CHIIR'16, and best paper honorable mention in SIGIR'15. He also served as a co-organizer for NTCIR-11/12 IMine task, TREC FedWeb 2014 task, Heterogeneous Information Access (HIA) workshop at WSDM'15 & SIGIR'16, and AIRS'16 Poster and Demo chair. Prior to joining University of Nottingham, he was a research scientist working in user engagement & ad quality science team in Yahoo Research London. He was previously a research associate in Language Technology Group in University of Edinburgh, working on text mining and information retrieval from 2013. Prior to this, he has conducted his PhD research on evaluation of aggregated search at the Information Retrieval Group in University of Glasgow. More details can be found at https://sites.google.com/site/keadamzhou/.

Title: Modeling User Engagement for Ad and Search

Abstract：

In the online world, user engagement is a key concept in designing user-centered web applications. It refers to the quality of the user experience that emphasizes the phenomena associated with wanting to use an application longer and frequently. In this talk, I will present my past efforts in modeling user engagement in the context of ad and search, seeking to provide insights on how to make an engaging experience. Firstly, to ensure long-term user engagement with Yahoo, I will present a learning framework that effectively identify ads with low quality. Secondly, in the context of search, I will talk about understanding and modeling user examination and satisfaction of the search result pages.

演讲人：清华大学刘奕群副教授

讲座时间：1月6日（周五）15:50

讲座地点：信息楼四楼学术报告厅

Yiqun Liu is now working as associate professor at 1 Department of Computer Science and Technology in Tsinghua University, Beijing, China. His major research interests are in Web Search, User Behavior Analysis, and Natural Language Processing. He is also a Principal Investigator (PI) of a joint Center (named NExT) between National University of Singapore and Tsinghua University to develop technologies for live media search. He serves in the editorial board of the Information Retrieval Journal (Springer). He also serves as short paper chair of SIGIR2017, program chair of NTCIR-13, general chair of AIRS2016 as well as program committee members of a number of important international academic conferences including SIGIR, WWW, AAAI, ACL and IJCAI. He published over 30 papers in top-tier academic conferences/journals and got over 1,600 citations according to Google scholar. He received the best paper honorable mention award of SIGIR2015 and AIRS2013. He has also been the coordinator for the NTCIR INTENT and IMine tasks since 2011.

Title: Understanding and Predicting Search Satisfaction in a Heterogeneous Environment

Abstract:

Search performance evaluation can be performed using metrics based on result relevance or alternative measures based on users’ search experience. Recent studies indicate that relevance-based evaluation metrics, such as MAP and nDCG, may not be perfectly correlated with users’ search experience (usually considered as the gold standard). Therefore, search satisfaction has become one of the prime concerns in search evaluation studies. In this talk, I will discuss about some of our recent progresses in the understanding and effective prediction of search satisfaction. I will start by talking about the relationship between relevance, usefulness and satisfaction. More specifically, how do document’s usefulness perceived by the user and relevance annotated by the assessors correlate with user’s satisfaction? After that, we investigate users’ satisfaction perception in a heterogeneous search environment and try to find out how vertical results on SERPs affect users’ satisfaction. Finally, we introduce a novel satisfaction prediction framework which relies on users’ mouse movement patterns (motifs) to identify satisfied or unsatisfied search sessions.

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