Web Hashtag Hashtag Twitter Hashtag Twitter Hashtag Hashtag Hashtag Twitter Hashtag Twitter Hashtag contexthashtag contexthashtag Hashtag contexthasht

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1 DEIM Forum 2011 F5-4 contexthashtag Twitter kaieda@coms.ics.ritsumei.ac.jp, huang@fc.ritsumei.ac.jp, kawagoe@is.ritsumei.ac.jp contexthashtag Twitter Twitter Twitter Hashtag Hashtag Hashtag Hashtag contexthashtag Hashtag contexthashtag contexthashtag Hashtag Hashtag Twitter Hashtag Abstract An Event Recommendation System for Twitter Users Using the contexthashtag Takahiro KAIEDA, Hung-Hsuan HUANG, and Kyoji KAWAGOE Graduate School of Science and Engineering, Ritsumeikan University Nojihigashi 1 1 1, Kusatsu, Shiga, Japan Colledge of Information Science and Engineering, Ritsumeikan University Nojihigashi 1 1 1, Kusatsu, Shiga, Japan kaieda@coms.ics.ritsumei.ac.jp, huang@fc.ritsumei.ac.jp, kawagoe@is.ritsumei.ac.jp In this paper, we propose an event recommendation system for Twitter users using contexthashtag. Twitter users can get rich event information by Hashtags benefitting from the widespread of Twitter. However, event retrieval merely using Hashtags may not meet the user s expectation due to Hashtags property where they can be defined independently by different users and lack coincidence. In order to solve this problem, we propose contexthashtag that forms a structured event-hashtag space where an existing event Hashtag can be located as a region. The system then recommends the user new events based on similarity comparison of contexthashtags between new events and ones of the events which the user ever participated. Key words Twitter Hashtag Text Classification Recommendation System 1. eventpage.jp 1 Twitter 2 [1] [2] [3] [4] Web Twitter Hashtag Hashtag Twitter hanabi ) Hashtag Hashtag Hashtag Web hashtagsjp Hashtag

2 Web Hashtag Hashtag Twitter Hashtag Twitter Hashtag Hashtag Hashtag Twitter Hashtag Twitter Hashtag contexthashtag contexthashtag Hashtag contexthashtag Hashtag Hashtag Hashtag Twitter 2. Hashtag contexthashtag 2. 1 Hashtag Twitter Hashtag Hashtag 3 Hashtag Twitter Twitter ricebowl Twitter Hashtag ricebowl Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag NHK Hashtag nhk kouhaku61 kouhaku Twitter Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag 歌手倉木麻衣 #Mai #Mai&Ayumi 浜崎あゆみ #Ayumi 開催日時年 ) 倉木麻衣 #Mai 1 contexthashtag #Mai&Ayumi[2009, 1, 1, ]t 開催日時年 ) 1 浜崎あゆみ #Ayumi[2010, 0, 1, ]t 1 [2007, 1, 0, ]t 2 contexthashtag Hashtag Twitter 2. 2 contexthashtag contexthashtag Hashtag Hashtag Hashtag Hashtag contexthashtag Hashtag contexthashtag Wikipedia 4 Hashtag Hashtag Hashtag contexthashtag contexthashtag 1 1 contexthashtag 1 1 contexthashtag Hashtag Ayumi 2010 Mai 2007 Mai&Ayumi 2009 AKB AKB

1 Hashtag Twitter Hashtag Hashtag 3 Hashtag Twitter Twitter ricebowl Twitter Hashtag ricebowl Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag 2010 12 31 NHK Hashtag nhk

3 Hashtag 1 contexthashtag ) 1 1 Hashtag Ayumi ) =, 2010) Ayumi Mai& Ayumi Mai 2 contexthashtag Hashtag Hashtag Mai&Ayumi contexthashtag AKB AKB48 Hashtag AKB 2011 Hashtag Mai 2011 Hashtag contexthashtag Hashtag Hashtag contexthashtag Hashtag Hashtag Hashtag 3. contexthashtag Twitter contexthashtag Hashtag Hashtag Hashtag contexthashtag Hashtag URL 3 Hashtag Hashtag 5 hashtagsjp Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag 2 Hashtag contexthashtag Hashtag Hashtag contexthashtag Hashtag 3. 1 Hashtag Hashtag Hashtag contexthashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag 3. 2 contexthashtag contexthashtag [5] contexthashtag Hashtag Hashtag contexthashtag contexthashtag Hashtag Hashtag h i N Hashtag H H = {h 1, h 2,, h N contexthashtag g k M contexthashtag G G = {g 1, g 2,, g M contexthashtag g k n k E k = {E k1,, Eknk E k j Hashtag H contexthashtag H = H 1 H 2 H N g k H k h k j Hk h k j {e = k 1,, ek n k ) e k i Ẽi k Ei ki = 1,, n k) 1) h k j gk 2) h k j contexthashtag gk Hashtag Ẽ k i E k i contexthashtag g k Ig k 5

contexthashtag Twitter contexthashtag Hashtag Hashtag Hashtag contexthashtag Hashtag 3 1 20 1 URL 3 Hashtag Hashtag 5 hashtagsjp Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag 2 Hashtag

4 Ig k Hashtag h k j ek i contexthashtag g k Ei k 3. 3 Hashtag contexthashtag Hashtag 1) E k 3 contexthashtag contexthashtag g k Hashtag h k j p j e j f j h k j = p t j e j f j t 3) t Geocoding API 6 Geocoding API XML contexthashtag g k Hashtag p j p jx, p jy ) p j = p jx, p jy 4) contexthashtag g k Hashtag f j f ji i = 1,..., n k ) f j = f j1,..., f jnk 5) 3. 4 Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag contexthashtag Hashtag h i Hashtag w j 2 h i w j S imh i, w j ) 6) 0 < = S imh i, w j ) < = 1 Hashtag h i p i e i f i Hashtag w j p j e j f j S imh i, w j ) = S im p p i, p j ) S im f f i, f j ) S im e e i, e j ) 6) S im p p i, p j ) 7) S im f f i, f j ) 8) S im p p i, p j ) 1 S im f f i, f j ) S im p p i, p j ) = S im f f i, f j ) = dp i, p j ) f i f j f i f j dp i, p j ) 7 100km ) S im e e i, e j ) 9) α F x e i, e j ) x contexthashtag ) e i e j S im e e i, e j ) F x e i, e j ) 1 7) 8) f ji Hashtag i 1 Hashtag i ) f ji = 0 ) contexthashtag $concert Hashtag UH WL $concert f j =, AKB48, UH WL Hashtag , , 2010/12/8, 0, 0, 1 ) 1 1 α S im e e i, e j ) = 9) F x e i, e j ) + 1 α 1 α = 4 4. contexthashtag Twitter

4 Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag Hashtag contexthashtag Hashtag h i Hashtag w j 2 h i w j S imh i, w j ) 6) 0 < = S imh i, w j ) < = 1 Hashtag h i p i e i f i Hashtag w j p j e j f j

5 1. 前処未理来イベントHashtagをイベント推薦システム取得 Hashtag 検索サイトメッセージを利 STEP1 3.contextHashtag 2. contexthashtagに基本データをもとに空分間対類内応にする位置付け基 URL 本イベントHashtag データ,メッセージログイン用者の過去取イベントHashtag 得付き contexthashtag DB 利用者 Hashtagと利 STEP2 類似用度者をが使未用来したイベントHashtag 全ての過去イベント間のユーザDB イベントとメッセージ情報絞 STEP2で STEP3 り込み算出した値を閾値により Twitter 3 3 Twitter Hashtag Twitter contexthashtag Hashtag contexthashtag contexthashtag Hashtag Twitter Hashtag Hashtag Hashtag URL Hashtag contexthashtag Hashtag contexthashtag STEP1 Hashtag STEP2 STEP1 contexthashtag Hashtag Hashtag STEP3 STEP2 Hashtag 4. 2 contexthashtag contexthashtag Hashtag Hashtag 100 Hashtag 3 Hashtag contexthashtag 緯度経度 A1 B 1 C D E F G H I 開催日時 ) Hashtag 1996MaiAyu Hashtag 3 5 Hashtag 1992S azan Hashtag 3 6 Hashtag 1991S map Hashtag SMAP Hashtag Hashtag 5 2 Hashtag Hashtag ) 1996MaiAyu S azan S map SMAP , , , , , , , , ,

ての過去イベント間のユーザDB イベントとメッセージ情報絞 STEP2で STEP3 り込み算出した値を閾値により Twitter 3 3 Twitter Hashtag Twitter contexthashtag Hashtag contexthashtag contexthashtag Hashtag Twitter Hashtag Hashtag

6 4 1 ) EXILE ) ) SMAP SMAP SMAP contexthashtag contexthashtag Java Twitter API Java Twitter4J contexthashtag Hashtag Hashtag Hashtag contexthashtag contexthashtag Hashtag Hashtag25 Hashtag Hashtag Hashtag 0.05 contexthashtag halcali contexthashtag f j =,, halcali, Per f ume,,, ) halcali HALCARI Hashtag , , 1, 0, 0,, Hashtag Hashtag F contexthashtag Hashtag Hashtag 3 1 ) 4 ) F F Hashtag 8 Hashtag F

7 5 1 contexthashtag 2 6. contexthashtag Twitter contexthashtag Hashtag Hashtag Hashtag Twitter contexthashtag Hashtag Twitter hashtagsjp [1] Akshay Java, Xiaodan Song, Tim Finin, Belle Tseng Why we twitter: understanding microblogging usage and communities WebKDD/SNA- KDD 07 Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pp , [2] Owen Phelan, Kevin McCarthy, Barry Smyth Using Twitter to Recommend Real-Time Topical News RecSys 09 Proceedings of the third ACM conference on Recommender systems, pp , [3] Miles Efron Hashtag Retrieval in a Microblogging Environment SIGIR 10 Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp , [4], Twitter Tweet, Web, 2010, 7, pp , [5], Vol.19, No.3, pp , 2004.

microblogging usage and communities WebKDD/SNA- KDD 07 Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pp. 56-65, 2007.

[1] HITS EigenRumor Web PageRank 情報の要求投稿者推薦システム投稿者の重要度推定 ( 本研究 ) の引用回数から推定投稿者のネットワークから推定個人的な興味を考慮した部分 1 投稿者のランキング Web EigenRumor Kri

[1] HITS EigenRumor Web PageRank 情報の要求投稿者推薦システム投稿者の重要度推定 ( 本研究 ) の引用回数から推定投稿者のネットワークから推定個人的な興味を考慮した部分 1 投稿者のランキング Web EigenRumor Kri DEIM Forum 21 C3-4 66-81 66-81 E-mail: spzuka@db.soc.i.kyoto-u.ac.jp, {ysuzuki,yoshikawa}@i.kyoto-u.ac.jp A calculation method of blogger s importance using influences to others in micro-blogs Kazuki YOSHIMOTO,