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$B"##3"#(B CIKM 2017 $B;22CJs9p(B $B!!!!!!!!!!!!!!!!!!!!!!!!!!!!M{5HV((B ($B5~ETBg3X(B)
The 26th ACM International Conference on Information and Knowledge
Management (CIKM2017) has just been held in Singapore at November 6-10,
2017. I am very glad to be able to attend this high-level international
conference. Instead of introducing the detailed papers related to my
research area, i.e., crowdsourcing and human computation, I would like
to introduce some general issues, which may be interested by more readers
of this report.
1. Conference and Singapore
In contrast to the conferences with researchers from single community,
CIKM contains attendances from diverse communities including ML/DM, IR,
DB/DE, NLP and so on. As claimed by the organizers, this conference is
growing on both the size and topic diversity, and will continue to grow
in the near future. There are more than 700 attendees from more than 40
countries this year. The total number of submission of full papers is
855 (701 in 2016 and 646 in 2015). The acceptance rate is 171/855 (20%)
for full papers and 119/419 (28%) for short papers. The theme of the
conference this year is smart cities and smart nations. In the keyword
cloud of the papers, graph/network, entity, semantic and recommendation
are some of the major topics. CIKM2018 will be held in Turin, Italy.
CIKM2019 will be held in Beijing, China.
For the impression of Singapore, both the foods provided by the conference
and in the streets are very delicious. In this country, it is very easy
to find the foods from various Asian countries. Singapore is mainly a
financial center, while there are IT companies located at Singapore which
focus on the southeast Asian market.
2. Keynotes
- The talk given by Dr. Rajeev Rastogi from Amazon introduced the machine
$B!!(Blearning research and applications in Amazon, including product demand
$B!!(Bforecasting, product classification, product matching, information
$B!!(Bextraction from reviews, question answering system and so on. In
addition, one of interesting topics was to standardize the diverse shoe
sizes across brands and locales. These research topics related to
practical requirements are very interesting and useful.
- Dr. Qiang Yang from Hong Kong University of Science and Technology
gave a talk on deep transfer learning, which adapted the model trained
from the data from a task/domain to other data from other tasks/domains.
It is one of the hot topics in recent years for various AI research
areas. He is one of the most famous researchers on this topic.
- Dr. Rada Mihalcea from University of Michigan talked about deception
$B!!(Bdetection, which tried to detect whether a person lied or told the
$B!!(Btruth. In the early years, they started this research only based on
$B!!(Btext; after that they considered the language from different culture;
$B!!(Bthen they focused on multi model information including motion and
facial expression in the video as well as text. Their methods showed
better performance than human beings on deception detection. They also
considered language demographics and fake news in recent work.
3. Best Paper Award
Y. Zhuang et al "Hike: A Hybrid Human-Machine Method for Entity Alignment
in Large-Scale Knowledge Bases". The authors are from Tsinghua unviversity,
UC Irvine and the University of Hong Kong. It is to align the entities
when integrating the heterogeneous Knowledge Bases (KBs). Due to the
inconsistency and uncertainty of large-scale KBs, automatic techniques
for KBs alignment achieve low quality. This paper thus proposes a hybrid
human-machine framework by harnessing the crowd to improve the alignment
quality. It is a solid work on such type of crowdsourcing and human
computation topics. It proposes a rational solution which covers the
required issues in such topics including crowdsourcing framework, cost
optimization and quality control. At the beginning, I thought it was a
similar work with "Reducing Uncertainty of Schema Matching via
Crowdsourcing" in VLDB 2013 (not referred by this paper). This VLDB2013
paper also proposed a hybrid method and asked crowd to match the elements
from two schemata (e.g., database schemata, XML schemata). However,
because the entities in knowledge graph are different the elements in
schemata. The authors of this CIKM2017 paper propose a different framework
and focus on specific issues for this entity and knowledge graph scenario
such as entity partition in knowledge graph to reduce the cost of crowd.
4. AnalytiCup 2017
It contains four data competitions which lasts nine months in total.
They are Shenzhen Meteorological Bureau-Alibaba Short-Time Quantititative
Precipitation Forecasting Challenge; DataSpark Mobility Open-Task Challenge;
Lazada Product Title Quality Challenge; DHL Temperature-Controlled Supply
Chain Hackathon Challenge. There are 1500 teams from all over the world.
Specially, the Dataspark company provides some useful mobility data of
Singapore, e.g., "footfall" is the unique count of people appearing at
a given place and time; "origin-destination" is the unique count of people
traveling from a given origin to a given destination area; "dwell time"
is the average time spent by visitors who entered a given location at
a given hour or day. These data are not easy to obtain for the academic
researchers in the universities and very useful for some potential
research topics.
This is a brief introduction of the programs in the CIKM 2017. If you
are interested in any issues and have questions, please feel free to
contact me.
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=======================================================================
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$B2&(B $B8585(B
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y.wang(a)yamaguchi-u.ac.jp
circl.wang(a)gmail.com
Yuanyuan Wang, Ph.D
Assistant Professor, Information Science and Engineering Section,
Department of Engineering,
Graduate School of Sciences and Technology for Innovation, Yamaguchi
University, Japan$B!!(B
E-mail: y.wang(a)yamaguchi-u.ac.jp
circl.wang(a)gmail.com
