著者
大澤 昇平 松尾 豊
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
vol.29, no.5, pp.469-482, 2014-09-01 (Released:2014-08-15)
参考文献数
22
被引用文献数
1

In social networking service (SNS), popularity of an entity (e.g., person, company and place) roles an important criterion for people and organizations, and several studies pose to predict the popularity. Although recent papers which addressing the problem of predicting popularity use the attributes of entity itself, typically, the popularity of entities depends on the attributes of other semantically related entities. Hence, we take an approach exploiting the background semantic structure of the entities. Usually, many factors affect a person's popularity: the occupation, the parents, the birthplace, etc. All affect popularity. Predicting the popularity with the semantic structure is almost equivalent to solving the question: What type of relation most affects user preferences for an entity on a social medium? Our proposed method for popularity prediction is presented herein for predicting popularity, on a social medium of a given entity as a function of information of semantically related entities using DBpedia as a data source. DBpedia is a large semantic network produced by the semantic web community. The method has two techniques: (1) integrating accounts on SNS and DBpedia and (2) feature generation based on relations among entities. This is the first paper to propose an analysis method for SNS using semantic network.
著者
榊 剛史 松尾 豊
出版者
人工知能学会
雑誌
人工知能学会全国大会論文集 (ISSN:13479881)
巻号頁・発行日
vol.24, 2010

近年、マイクロブログサービスであるTwitterが脚光を浴びており、ビジネス・研究分野において、様々な分析やサービスが発表されている。本論文ではTwitterのリスト機能に注目する。リスト機能は、ユーザーを整理するための機能であるが、同時にユーザーへのソーシャルブックマーク(SBM)と捉えることができる。既存のSBM分析の手法を適用することで、Twitterユーザーの属性抽出や特徴語抽出を行う。
著者
松尾 豊 友部 博教 橋田 浩一 中島 秀之 石塚 満
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
vol.20, no.1, pp.46-56, 2005 (Released:2005-01-05)
参考文献数
20
被引用文献数
13 28

Social relation plays an important role in a real community.
著者
松尾 豊 安田 雪
出版者
人工知能学会
雑誌
人工知能学会論文誌 = Transactions of the Japanese Society for Artificial Intelligence : AI (ISSN:13460714)
巻号頁・発行日
vol.22, pp.531-541, 2007-11-01
被引用文献数
30 12

Our purpose here is to (1) investigate the structure of the personal networks developed on mixi, a Japanese social networking service (SNS), and (2) to consider the governing mechanism which guides participants of a SNS to form an aggregate network. Our findings are as follows:the clustering coefficient of the network is as high as 0.33 while the characteristic path lenght is as low as 5.5. A network among central users (over 300 edges) consist of two cliques, which seems to be very fragile. Community-affiliation network suggests there are several easy-entry communities which later lead users to more high-entry, unique-theme communities. The analysis on connectedness within a community reveals the importance of real-world interaction. Lastly, we depict a probable image of the entire ecology on mixi among users and communities, which contributes broadly to social systems on the Web.
著者
松尾 豊 石塚 満
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
vol.17, no.3, pp.217-223, 2002 (Released:2002-04-04)
参考文献数
24
被引用文献数
6 18

We present a new keyword extraction algorithm that applies to a single document without using a large corpus. Frequent terms are extracted first, then a set of co-occurrence between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. The distribution of co-occurrence shows the importance of a term in the document as follows. If the probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of the frequent terms, then term a is likely to be a keyword. The degree of the biases of the distribution is measured by χ²-measure. We show our algorithm performs well for indexing technical papers.
著者
大知 正直 松尾 豊
出版者
人工知能学会
雑誌
人工知能学会全国大会論文集 (ISSN:13479881)
巻号頁・発行日
vol.27, 2013

近年,ソーシャルネットワークを利用したコミュニケーションがますます重要になってきている.企業や情報を発信したい個人にとって,いかに自分の発言の影響を最大化するかは,大きな課題のひとつである.本研究では,ソーシャルメディア上での影響を適合度関数とし,その環境下で交叉と突然> 変異を繰り返すことで進化する人工生命を作り出す手法を提案し,影響力の高いユーザが何を最適化しているのかを明らかにする,
著者
大知 正直 関 喜史 川上 登福 小野木 大二 野村 眞平 吉永 恵一 松尾 豊
出版者
人工知能学会
雑誌
人工知能学会全国大会論文集 (ISSN:13479881)
巻号頁・発行日
vol.27, 2013

近年,ユーザはウェブ上の情報を重視して購買するようになっている.Eコマース関連企業はユーザの購買意思決定のプロセスをウェブ上に蓄積された行動履歴から分析している.これまでの分析は多くのユーザが同一商品を購入できる商材を対象にしてきたが,本稿では,住宅販売市場を対象とする. 住宅は全て別々の商品であり,多くのユーザは一度しか購入しない.本研究ではこうした商材に対するユーザ行動の特性を明らかにする.
著者
松尾 豊 安田 雪
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
vol.22, no.5, pp.531-541, 2007 (Released:2007-07-17)
参考文献数
22
被引用文献数
10 12 28

Our purpose here is to (1) investigate the structure of the personal networks developed on mixi, a Japanese social networking service (SNS), and (2) to consider the governing mechanism which guides participants of a SNS to form an aggregate network. Our findings are as follows:the clustering coefficient of the network is as high as 0.33 while the characteristic path lenght is as low as 5.5. A network among central users (over 300 edges) consist of two cliques, which seems to be very fragile. Community-affiliation network suggests there are several easy-entry communities which later lead users to more high-entry, unique-theme communities. The analysis on connectedness within a community reveals the importance of real-world interaction. Lastly, we depict a probable image of the entire ecology on {\\em mixi} among users and communities, which contributes broadly to social systems on the Web.
著者
大澤 昇平 松尾 豊
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
pp.A-F24, (Released:2016-01-06)
参考文献数
22

Success of software developping project depend on skills of developers in the teams, however, predicting such skills is not a obvious problem. In crowd sourcing services, such level of the skills is rated by the users. This paper aims to predict the rating by integrating open source software (OSS) communities and crowd soursing services. We show that the problem is reduced into the feature construction problem from OSS communities and proposes the s-index, which abstract the level of skills of the developers based on the developed projects. Specifically, we inetgrate oDesk (a crowd sourcing service) and GitHub (an OSS community), and construct prediction model by using the ratings from oDesk as a training data. The experimental result shows that our method outperforms the models without s-index for the aspect of nDCG.