prediction io–final 2014-jp-handout
TRANSCRIPT
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Yuki Furuta Naoto Yamamoto Tran Hoan
Facebook Open Academy International
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What is ?An open source machine learning server
For software developers to create predictive features in their web and mobile app.
Currently powering thousands of developers and hundred of applications1
1 http://github.com/PredictionIO/
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Architecture
Horizontally scalability
Spark
Data Preparator
Model 1
Model N
HBaseQuery
PredictionResultData
SourceImport Data(EventServer)
Algorithm 1
Algorithm N
ServingHDFS
Spark
.
.
.
http://docs.prediction.io/resources/systems/
Web AppMobile App
Productivity
Data In Data Out
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What can do?
Content-based recommendationTrend detection Sentiment Analysis
Restaurant recommendation User similarity
Data analysisEngine
(recommendation, rank,…)
YELPIO-NAVI3
MovieLens
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YELPIO-NAVI
Naoto Yamamoto Tran Hoan
Recommendation App for RestaurantsUsing Yelp! Dataset
Inhwan Eric Lee(JP)(JP)(USA)
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What is YELPIO-NAVIYelp:
食べログ in America
Information ofrestaurants’ address, stars
users’ stars
Recommendation of RestaurantsUsing These Information
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YELPIO-NAVI Demo Setup
Batch import datathrough RubySDK
Store & RetrieveBusiness Data
Retrieve & StoreBusiness Data
through REST API
Retrieve Prediction Results through REST API
https://github.com/OminiaVincit/predictionio_rails
http://yelpio.hongo.wide.ad.jp/
https://github.com/OminiaVincit/YELPIO_demo2
(1) Neighbourhood model(2) Collaborative Filtering
http://www.yelp.com/
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YELPIO-NAVI Demo
http://yelpio.hongo.wide.ad.jp/
7 http://zorovn.hongo.wide.ad.jp/
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MovieLensContent-based Movie Recommendation
Yuki FurutaNhu-Quynh Beth Yue ShiShaocong Mo(JP)(USA) (USA) (USA)
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x MovieLens- Content-Based Movie Recommendation Engine -
A B
A. Collaborative Filtering
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x MovieLens
MovieLens Datasets• 100,000 ratings (1-5)
from 943 users on 1682 movies
• Simple demographic info for the users (age, gender, occupation, zip)
• Information about the movies (title, release date, genre)
- Content-Based Movie Recommendation Engine -B. Content-Based
A (age: 20, male, RUS) B (age: 21, male, KZH)
20-year-old man likes:• Action 60%• Comedy 10%• English 10%• etc.
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x MovieLens- Content-Based Movie Recommendation Engine -
Datasetval DataSourceAttributeNames = AttributeNames( user = "pio_user", item = "pio_item", u2iActions = Set("rate"), itypes = "pio_itypes", starttime = "pio_starttime", endtime = "pio_endtime", inactive = "pio_inactive", rating = "pio_rating")
Feature Based
User Based
Algorithms
PreparationReading DataQuery
Serve
MovieLens - User (ID, Age, Gender, Occupation, Zip) - Movie (ID, Title, Year, Genre, Actors,…)
Prepare Train
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x MovieLens- Content-Based Movie Recommendation Engine -
Stanlay KubricksAmericaComedy
BlackSF
Rowan AtkinsonUnited Kingdom
ComedySF
Action
Feature Based Algorithm
Michael
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x MovieLens- Content-Based Movie Recommendation Engine -
Stanlay KubricksUSA
ComedyBlack
ScientificFantasy
Rowan AtkinsonUnited Kingdom
ComedySF
ActionFantasy
ComedyFantasyActionUSA
Mark WahlbergUSA
ComedyFantasyAction
Recommend!
Feature Based Algorithm
Michael
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x MovieLens- Content-Based Movie Recommendation Engine -Feature Based Algorithm
UserID: 1, Age: 24, Gender: M, Occupation: technician, Zip: 85711 UserID: 2, Age: 53, Gender: F, Occupation: other, Zip: 94043 UserID: 3, Age: 23, Gender: M, Occupation: writer, Zip: 32067 UserID: 4, Age: 24, Gender: M, Occupation: technician, Zip: 43537 UserID: 5, Age: 33, Gender: F, Occupation: other, Zip: 15213
User: 196 rates Movie: 242 (3.0 / 5) User: 186 rates Movie: 302 (3.0 / 5) User: 22 rates Movie: 377 (1.0 / 5) User: 244 rates Movie: 51 (2.0 / 5) User: 166 rates Movie: 346 (1.0 / 5)
Threshold (e.g. 2.0)BUY BUY - - -
Train
Querye.g. Recommend 5 movies for UserID: 2 Recommend 5 movies which are “Comedy” for UserID:2 Recommend 2 movies which are “Action” by Rowan Atkinson for UserID: 2
1. MovieID: 297 Score: -8.53295620539528 2. MovieID: 251 Score: -13.326537513274323 3. MovieID: 292 Score: -15.276804370241758 4. MovieID: 290 Score: -32.944167483781335 5. MovieID: 314 Score: -37.45527366828404
Predict
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…to be continued
Scale for Big Data
Multi-engines & Multi-algorithms
Predict with more features
…
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Evaluation
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Thank you for listening
Japanese team
Yuki Furuta Naoto Yamamoto Tran Hoan