44. 効果高い課題設定・解決を行う
状況に合わせ適切な方法を
選択・組合せ検証する
Standard Recommendation Methodologies
Memory Based Paradigm
Model Based Paradigm
(most user decision focused)
> 協調フィルタリング (users that play A play B)
> ソーシャルグラフ (user neighborhood)
> 強化学習 (user feedback)
> locality sensitive hashing (user profile similarity)
(most detailed experiments and rationale)
> パターンの学習と予測
> latent semantic analysis (game text similarity)
> artificial neural network
Emergent Intelligence Paradigm
Hybridized Intelligence Paradigm
(fastest adaptation)
> エージェントランダムウォーク(user similarity search)
> 遺伝的アルゴリズム (game preference convergence)
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(most successful globally)
> 手法の組合せによる向上
>> 友人がいないとき (e.g. new user)
>> 利用データが存在しないとき (e.g. new game)
>> モデルからメタデータが取得できないとき
> 混合手法によるこれらの課題解決
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45. 活動例(抜粋): Recommendation
Social Collaborative Filtering
ソーシャルグラフを用いたユーザー行動履歴・グラフによる推薦
Social Graph
Input User History
Collaborative Filtering
(Global)
(Global Matrix Model for “A likes B”)
New user with no history
RECS
and Social Graph
Input User History and
Friend
Neighborhood
Collaborative Filtering
(Personal)
(Neighborhood Matrix Model for “A likes B”)
RECS
user
User with history
Neighborhood
Item-Item Matrix of Relationships (Invented by Amazon.com)
Friend of Friend
Wikipedia Image of a Social Network
There are thousands of collaborative filtering varieties:
+ user friend neighborhood…
+ user similarity clustered neighborhood…
Relationship between Game A2 and C2
Users who “bought A bought C”, “viewed A viewed C”…
References (International Research Copyrights)
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46. 活動例(抜粋): Recommendation
Social Neural Networks
ソーシャルグラフを用いたユーザー行動履歴・グラフによる推薦
Social Graph
1
G1
G1
0
1
G2
G2
0
0
G3
G3
0
0
G4
G4
1
0
G5
G5
0
0
G6
G6
0
0
G7
G7
0
0
G8
G8
0
0
G9
G9
0
Friend
user
Input User History
and Neighborhood
Neighborhood
Friend of Friend
HISTORY
RECOMMENDATIONS
Wikipedia Image of a Social Network
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46
102. 大規模データマイニング基盤
Data Mining Libraries
各種ソーシャル行動解析用のデータマイニングライブラリ
Data-mining
Machine-Leaning
Results
Data Mining Infrastructure
KPI Inspection
DeNA Data Mining Libraries
Data Mining/Machine Learning
Mahout
…
MapReduce
Morphological Analysis
DeNA Social MA
KPI Views
R
Perl
Pig
Java
Hive
HUE
Pre-processing/Indexing
Lucene
Service
Log API
Service
Log API
Business
Planning
Service
…
Log API
…
Hadoop DFS
Unified Description of
Action/Status Log
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102
103. 大規模データマイニング基盤
データマイニング・機械学習による
迅速なサービス洗練を実現しています
Data-mining
Machine-Leaning
Results
Data Mining Infrastructure
KPI Inspection
DeNA Data Mining Libraries
Data Mining/Machine Learning
Mahout
…
MapReduce
Morphological Analysis
DeNA Social MA
KPI Views
R
Perl
Pig
Java
Hive
HUE
Pre-processing/Indexing
Lucene
Service
Log API
Service
Log API
Business
Planning
Service
…
Log API
…
Hadoop DFS
Unified Description of
Action/Status Log
DeNA Co.,ltd. ALL rights reserved
103
104. 大規模データマイニング基盤
統一行動記述
Data-mining
Machine-Leaning
Results
Data Mining Infrastructure
KPI Inspection
DeNA Data Mining Libraries
Data Mining/Machine Learning
Mahout
…
MapReduce
Morphological Analysis
DeNA Social MA
KPI Views
R
Perl
Pig
Hive
Hive
Pig
Pre-processing/Indexing
Lucene
Service
Log API
Service
Log API
Business
Planning
Service
…
Log API
…
Hadoop DFS
Unified Description of
Action/Status Log
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104
130. 関連資料
“Mobageの大規模データマイニング” - PRMU 2011 Big Data and Cloud, 2011/10/6
http://www.slideshare.net/hamadakoichi/mobage-prmu-2011-mahout-hadoop
“モバゲーの大規模データマイニング基盤におけるHadoop活用” - Hadoop Conference Japan 2011, 2011/2/22
http://www.slideshare.net/hamadakoichi/hadoop-hadoop-conference-japan-2011-hcj2011
DeNA Co.,ltd. ALL rights reserved
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