Category Archives: 未分类

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讲座信息:Information Access Evaluation: A Few Updates from the Sakai Laboratory

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时间: 11月17日 周五 上午10:00-11:30
地点:信息楼四层学术报告厅
题目: Information Access Evaluation: A Few Updates from the Sakai Laboratory
报告人: SAKAI Tetsuya

ABSTRACT
In this talk, I will first introduce the Sakai Laboratory in the Department of Computer Science and Engineering,  Waseda University. Our mission is “easy information access.”
Then I will provide a small sample from our research. I will talk briefly about our recent ACM CIKM 2017 short paper on ranking cards (verticals) for mobile search, which was a collaboration
project with Naver, the major search engine in Korea. Then I will spend more time on my ACM SIGIR 2017 full paper on  Bayesian approaches to evaluating information retrieval systems, as alternatives to classical significance tests.
BIO
Tetsuya Sakai is a professor and head of department at the Department of Computer Science and Engineering, Waseda University, Japan. He is also a visiting professor at the National Institute of Informatics. He joined Toshiba in 1993. He obtained a Ph.D from Waseda in 2000. From 2000 to 2001, he was supervised by the late Karen Sparck Jones at the Computer Laboratory, University of Cambridge, as a visiting researcher. In 2007, he joined NewsWatch, Inc. as the director of the Natural Language Processing Lab. In 2009, he joined Microsoft Research Asia. He joined the Waseda faculty in 2013. He was Associate Dean (IT Strategies Division) from 2015 to 2017. He is an editor-in-chief of the Information Retrieval Journal (Springer) and an associate editor of ACM TOIS. He is a general co-chair of NTCIR.


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讲座信息:人类交流新时代 今日头条的愿景与实践

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时间: 14:00-15:00, Oct. 18, 2017
地点:公教4109
题目: 人类交流新时代 今日头条的愿景与实践
演讲者: 李航

摘要:表达和传播自己的情感与思想是人类的基本诉求,交流和传递信息与知识是人类文明的重要基石,移动互联网和人工智能把我们带到了交流的全新时代;信息和知识在以前所未有的规模、速度、广度进行着传播与扩散,普适性,即时性,多样性,社交性成为其主要特点。在这个报告里,我将介绍今日头条子在人类交流新时代的愿景与实践,特别是在人工智能领域的挑战与机遇。我们的目标是成为“全球最懂你的信息平台,连接人与信息,促进创作与交流”。头条人工智能实验室正朝着这个目标,从事机器学习,计算视觉,自然语言处理等方面的研究,旨在开发业界最先进的人工智能技术,为用户提供最优质的服务。

主讲人简介:李航,今日头条人工智能实验室主任,北京大学,南京大学客座教授,IEEE会士,ACM杰出科学家,CCF高级会员。他的研究方向包括信息检索,自然语言处理,及数据挖掘。李航1988年日本京都大学电气工程系毕业,1998年获得日本东京大学计算机科学博士。他1990年至2001年就职于日本NEC公司中央研究所,任研究员。2001年至2012年就职于微软亚洲研究院,任高级研究员与主任研究员。2012年至2017年就职于华为技术有限公司诺亚方舟实验室,任首席科学家、主任。李航一直活跃在相关学术领域,曾出版过三部学术专著,并在顶级国际学术会议和顶级国际学术期刊上发表过120多篇学术论文,拥有42项授权美国专利,李航还在顶级学术会议和顶级国际学术期刊担任许多重要工作。


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讲座信息:Recent Advance on Recommendation Methods for Implicit Feedback

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时间: 14:00, Apr. 18, 2017
地点:信息楼417
题目: Recent Advance on Recommendation Methods for Implicit Feedback
演讲者: 何向南 National University of Singapore
Abstract: In recent years, the focus of recommender system research has shifted from explicit feedback problems such as rating prediction to implicit feedback problems. In this talk, I will introduce our recent two works for addressing the recommendation problem with implicit feedback.
In our first work [He et al. WWW 2017], we develop techniques based on neural networks for recommendation.
In our second work [Bayer and He et al. WWW 2017], we propose a novel and generic optimization method on par with BPR (Bayesian Personalized Ranking) for learning recommender models from implicit feedback. This work provides the theory and building blocks to derive efficient implicit CD algorithms for (multi-)linear recommender models.
Short Bio: Dr. Xiangnan He is currently a postdoctoral research fellow with the Lab for Media Search, National University of Singapore. His research interests span recommender system, information retrieval, multi-media and natural language processing. His works have appeared in several top-tier conferences such as SIGIR, WWW, MM, CIKM and AAAI, and top-tier journals including TKDE and TOIS. His work on recommender system has received the Best Paper Award Honorable Mention of ACM SIGIR 2016. Moreover, he has served as the PC member for the prestigious conferences including SIGIR, WWW, MM, CIKM and EMNLP, and invited reviewer for prestigious journals including TKDE, WWWJ and TIIS.

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讲座信息:Understanding and Predicting Search Satisfaction in a Heterogeneous Environment

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时间: 15:50, Sep. 6, 2016
地点:信息学院四层报告厅
题目: Understanding and Predicting Search Satisfaction in a Heterogeneous Environment
演讲者: Yiqun Liu

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.

Short Bio:Yiqun Liu is now working as associate professor at the 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.


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讲座信息:Modeling User Engagement for Ad and Search

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时间: 15:00 Jan. 6, 2017
地点:信息学院四层报告厅
题目: Modeling User Engagement for Ad and Search
演讲者: Ke(Adam) Zhou

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.

Bio:
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/.


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讲座信息:Approximate Counting via Correlation Decay

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时间: 14:00-15:30, Sep. 6, 2016
地点:信息学院四层报告厅
题目: Approximate Counting via Correlation Decay
演讲者: 陆品燕教授,上海财经大学信息学院

Abstract: In this talk, I will survey some recent development of approximate counting algorithms based on correlation decay technique. Unlike the previous major approximate counting approach based on sampling such as Markov Chain Monte Carlo (MCMC), correlation decay based approach can give deterministic fully polynomial-time approximation scheme (FPTAS) for a number of counting problems. The algorithms have applications in statistical physics, machine learning, stochastic optimization and so on.

Short Bio:Dr. Pinyan Lu is a professor and the founding director of Institute for Theoretical Computer Science at Shanghai University of Finance and Economics (ITCS@SUFE). Before joining SUFE, he was a researcher at Microsoft Research. He is also a Chair Professor at Computer Science Department and Zhiyuan College of Shanghai Jiao Tong University. He studied in Tsinghua University (BS (2005) and PhD (2009) both in Computer Science). He is interested in theoretical computer science, including complexity theory, algorithms design and algorithmic game theory. Currently, his research is mainly focused on complexity and approximability of counting problems, and algorithmic mechanism design.
http://itcs.sufe.edu.cn/pinyan/


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讲座信息:Discovering Neighborhood Pattern Queries by Sample Answers in Knowledge Base

Category : 未分类

时间: 14:00-15:00, May. 6, 2016
地点:信息学院四层报告厅
题目: Discovering Neighborhood Pattern Queries by Sample Answers in Knowledge Base
演讲者: Jialong Han博士, Nanyang Technological University

Abstract: Knowledge bases have shown their effectiveness in facilitating services like Web search and question-answering. Nevertheless, it remains challenging for ordinary users to fully understand the structure of a knowledge base and to issue structural queries. In many cases, users may have a natural language question and also know some popular (but not all) entities as sample answers. In this paper, we study the Reverse top-k Neighborhood Pattern Query problem, with the aim of discovering structural queries of the question based on: (i) the structure of the knowledge base, and (ii) the sample answers of the question. The proposed solution contains two phases: filter and refine. In the filter phase, a search space of candidate queries is systematically explored. The invalid queries whose result sets do not fully cover the sample answers are filtered out. In the refine phase, all surviving queries are verified to ensure that they are sufficiently relevant to the sample answers, with the assumption that the sample answers are more well-known or popular than other entities in the results of relevant queries. Several optimization techniques are proposed to accelerate the refine phrase. For evaluation, we conduct extensive experiments using the DBpedia knowledge base and a set of real-life questions. Empirical results show that our algorithm is able to provide a small set of possible queries, which contains the query matching the user question in natural language.

Short Bio:Jialong Han is a postdoctoral research fellow at School of Computer Science and Engineering, Nanyang Technological University. He earned his Ph.D. degree from Renmin University of China in 2015, under the supervision of Prof. Ji-Rong Wen. He obtained his B.E. degree also from Renmin University of China in 2010. His research interests include graph mining, graph data management, and knowledge bases.


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讲座信息:The Many Faces of Sequence Data Processing

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时间: 14:00-15:30, Apr. 20, 2016
地点:信息楼417会议室
题目: The Many Faces of Sequence Data Processing
演讲者: Tingjian Ge教授, 麻省大学洛威尔分校(UMass Lowell)

Abstract: Sequence data, also known as data streams, play an important role in data analytics research as well as Computer Science in general. Such data are prevalent: texts, biological sequences, ECG signals, time series, traffic sensory data, business and server logs, smartphone and social network data are just a few examples. In a broad sense, big data collected over time can be deemed as sequence data. A common type of analytical query over streams is pattern matching, also known as complex event processing.
A few complexities must be dealt with for real-world sequence data. For example, it may be produced at a high rate by unreliable devices and/or communicated through wireless networks (hence the data has noise). Moreover, patterns may need to take into consideration diverse semantics including parallel sub-patterns and graph structures. In this talk, I will describe a few lines of work we have carried out in the past few years on this topic. For pattern semantics, I discuss a few variants: subsequences, extended regular expressions, parallel and interleaving patterns, and subgraph-with-timing patterns. I also describe some algorithmic techniques to efficiently match the complex events in real time.

Short Bio:Tingjian Ge is an associate professor in the Computer Science Department of the University of Massachusetts, Lowell. He received a Ph.D. from Brown University in 2009. Prior to that, he got his Bachelor’s and Master’s degrees in Computer Science from Tsinghua University and UC Davis, respectively, and worked at Informix and IBM in California for six years. From 2009 to 2011 he worked as an assistant professor at the University of Kentucky. His research areas are in data management and big data analytics, with topics including noisy and uncertain data, data streams, and data security and privacy. He is a recipient of the NSF CAREER Award in 2012, and a Teaching Excellence Award at UMass Lowell in 2014. He often serves as a Program Committee member in major database and data mining conferences such as SIGMOD, ICDE, VLDB, and ICDM, and served as the Program Chair of New England Database Day 2015.


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郑凯教授学术交流活动

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3月4日下午,来自澳大利亚昆士兰大学郑凯博士在信息楼四层报告厅与老师和同学们进行了深入的学术交流,报告主题为Interactive Top-k Spatial Keyword queries。


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讲座信息:Large-scale Streaming Data Mining: Model Design and Application

Category : 未分类

时间: 14:00-15:30, Mar. 4, 2016
地点:信息楼四楼报告厅
题目: Interactive Top-k Spatial Keyword queries
演讲者: 郑凯讲师, 澳大利亚昆士兰大学

Abstract: Conventional top-k spatial keyword queries require users to explicitly specify their preferences between spatial proximity and keyword relevance. In this work we investigate how to eliminate this requirement by enhancing the conventional queries with interaction, resulting in Interactive Top-k Spatial Keyword (ITkSK) query. Having confirmed the feasibility by theoretical analysis, we propose a three-phase solution focusing on both effectiveness and efficiency. The first phase substantially narrows down the search space for subsequent phases by efficiently retrieving a set of geo-textual k-skyband objects as the initial candidates. In the second phase three practical strategies for selecting a subset of candidates are developed with the aim of maximizing the expected benefit for learning user preferences at each round of interaction. Finally we discuss how to determine the termination condition automatically and estimate the preference based on the user’s feedback. Empirical study based on real PoI datasets verifies our theoretical observation that the quality of top-k results in spatial keyword queries can be greatly improved through only a few rounds of interactions.

郑凯博士现为澳大利亚昆士兰大学计算机与电子工程系讲师。他在2012年被澳大利亚昆士兰大学授予计算机科学博士学位。郑凯博士主要研究如何为企业,科研和个人应用提供大数据管理,整合和分析的高效解决方案。他的主要研究领域包括社交媒体数据分析,时空数据库,不确定数据库, 数据挖掘和生物信息学。郑凯博士在CCF A类顶级国际期刊和会议上发表了超过40篇文章。他曾任APWeb 2016的程序委员会主席,SIGMOD (2015,2016),CIKM(2014,2015)程序委员和多个顶级期刊如IEEE TKDE, VLDB Journal和Geoinformatica的专家评审。郑凯博士是2013年澳大利亚优秀青年科研奖和2015年ICDE最佳论文奖的获得者。