Selection of housing, one of the necessities of human life, has a great influence on life for a long time. However, since it requires a wide range of information gathering and consideration before decision, state-of-the-art recommendation algorithms such as collaborative filtering do not work well. In this presentation, after reviewing issues specific to the real estate field, I cited examples of "application of crowdsourcing to social media (Twitter timelines)" and "application of deep learning to property images" as an effort by our research group. Finally I discuss what kind of AI technology is applicable in the real estate field.
ACM SIGMOD日本支部第56回支部大会でお話しした、ICDE 2014の参加報告についての資料です。以下のような6部構成になっています。全190ページです。
・ICDE 2014を俯瞰してみる(5p~)
・ビッグデータ時代の新発想:もうデータは蓄えない(32p~)
Keynote, Running with Scissors: Fast Queries on Just-in-Time Databases
・見えない相手と協調作業:センサネットワーク上のデータ集約(64p~)
10 Year Most Influential Paper, Approximate Aggregation Techniques for Sensor Databases
・メインメモリデータベースがハードウェアトランザクショナルメモリを使ったら…(96p~)
Best Paper, Exploiting Hardware Transactional Memory in Main-Memory Databases
・過去の結果を再利用:ビューを用いた大規模グラフからのパターン発見(126p~)
Best Paper Runner-up, Answering Graph Pattern Queries Using Views
・アルゴリズムでゴリゴリ解決:大量のベクトルから類似ペアを厳密に見つけたい(155p~)
気になる論文, L2AP: Fast Cosine Similarity Search With Prefix L-2 Norm Bounds
Selection of housing, one of the necessities of human life, has a great influence on life for a long time. However, since it requires a wide range of information gathering and consideration before decision, state-of-the-art recommendation algorithms such as collaborative filtering do not work well. In this presentation, after reviewing issues specific to the real estate field, I cited examples of "application of crowdsourcing to social media (Twitter timelines)" and "application of deep learning to property images" as an effort by our research group. Finally I discuss what kind of AI technology is applicable in the real estate field.
ACM SIGMOD日本支部第56回支部大会でお話しした、ICDE 2014の参加報告についての資料です。以下のような6部構成になっています。全190ページです。
・ICDE 2014を俯瞰してみる(5p~)
・ビッグデータ時代の新発想:もうデータは蓄えない(32p~)
Keynote, Running with Scissors: Fast Queries on Just-in-Time Databases
・見えない相手と協調作業:センサネットワーク上のデータ集約(64p~)
10 Year Most Influential Paper, Approximate Aggregation Techniques for Sensor Databases
・メインメモリデータベースがハードウェアトランザクショナルメモリを使ったら…(96p~)
Best Paper, Exploiting Hardware Transactional Memory in Main-Memory Databases
・過去の結果を再利用:ビューを用いた大規模グラフからのパターン発見(126p~)
Best Paper Runner-up, Answering Graph Pattern Queries Using Views
・アルゴリズムでゴリゴリ解決:大量のベクトルから類似ペアを厳密に見つけたい(155p~)
気になる論文, L2AP: Fast Cosine Similarity Search With Prefix L-2 Norm Bounds
「信頼フレームワーク最新動向」~Open Government, Open Economy, Open Identity~
2011年7月28日(木)
http://www.openid.or.jp/modules/news/details.php?bid=41
http://www.ustream.tv/recorded/16287210
「信頼フレームワーク最新動向」~Open Government, Open Economy, Open Identity~
2011年7月28日(木)
http://www.openid.or.jp/modules/news/details.php?bid=41
http://www.ustream.tv/recorded/16287210
Mining User Experience through Crowdsourcing: A Property Search Behavior Corp...Yoji Kiyota
This document describes a study that aimed to establish a method for understanding user experiences in property searching through analyzing Twitter timelines. The researchers collected Twitter timelines of followers of a Japanese property search service account and used crowdsourcing microtasks to extract tweets related to property searching and analyze them based on a conventional property search process framework. Workers were asked to categorize timeline fragments as either related or unrelated to property searching. This allowed the researchers to build a corpus of property search behavior data derived from social media for analyzing user needs and experiences.
1. IEEE
DSAA
2017
at
Tokyo
(The
4th
Intl.
Conference
on
Data
Science
and
Advanced
Analy<cs)
Sponsorship
Chairs
Yoji
Kiyota
(NEXT
Co.,
Ltd)
Kiyoshi
Izumi
(Univ.
of
Tokyo)
Tadashi
Yanagihara
(KDDI
Labs.)
Longbing
Cao
(Univ.
of
Technology
Sydney)
4. NLPに関連する主なTopics
• Informa<on
and
knowledge
retrieval,
and
seman<c
search
• Web/social/databases
query
and
search
• Personalized
search
and
recommenda<on
• Human-‐machine
interac<on
and
interfaces
• Crowdsourcing
and
collec<ve
intelligence
• Big
data
representa<on
and
visualiza<on
• Data
science
educa<on
and
training
prac<ces
and
lessons
• Large
scale
applica<on
case
studies
and
domain-‐specific
applica<ons
• Latent
seman<cs
and
insight
learning
• Cross-‐media
data
analy<cs
• Big
data
visualiza<on,
modeling
and
analy<cs
• Mul<media/stream/text/visual
analy<cs
• Personaliza<on
analy<cs
and
learning
• Web/online/social/network
mining
and
learning
6. Key
Dates
SIGIR
2017
Full
Paper
No6fica6on
of
Acceptance
2017/04/11
DSAA
2017
Paper
Submission
2017/05/25
No<fica<on
of
Acceptance
2017/07/25
Camera-‐Ready
2017/08/15
Conference
2017/10/19-‐21