project aura recommendation for the rest of us · • 60s • oldies • psychedelic •...

PROJECT AURARECOMMENDATION FOR THE REST OF USPaul Lamere, Senior Staff Engineer, Sun LabsStephen Green, Senior Staff Engineer, Sun Labs

TS-5841

2008 JavaOneSM Conference | java.sun.com/javaone | 2

In this talk you will learn how you can use Project Aura to deliver better recommendations for your content


Agenda

What is recommendation and why do we careWhat is wrong with existing recommendationsHow Project Aura fixes these problemsHow to use Project AuraDemonstration


The Problem: Long Tail Content

Huge amount of content on the web:• Videos, Music, Podcasts, Images, • News, Blogs, Wikis, Books

Much of this content is user-generated:• YouTube, Flickr, Digg, last.fm• Technorati, Delicious, eBay

Even more content is coming: IPTVKey to enabling Long Tail• Make everything available• Help me find it


How Important is Recommendation?

Netflix • 2/3 of movies rented were recommended• Offering $1 million prize for the first group to improve their

recommendations by 10%Amazon • 35% of product sales result from recommendations• Recommendations generated a couple of orders of magnitude

more sales than just showing top sellersGoogle News• Recommendations generate 38% greater click through


Extreme Commercial Interest

Image courtesy of Jadam Kahn


Using Aura – Quick Start – Code// Look up the user and the itemsUser fan = aura.getUser("http://openid.sun.com/plamere");

Item weezer = aura.findItemByName("Weezer");Item greenday = aura.findItemByName("Green Day");Item paris = aura.findItemByName("Paris Hilton");

// Pay attention to the itemsaura.attend(fan, weezer, Attention.RATE, Rating.FIVE_STARS);aura.attend(fan, greenday, Attention.PLAY);aura.attend(fan, paris, Attention.SKIP);

// Get recommendationsList<Recommendation> recs = aura.recommend(fan, ARTISTS, 3);for (Recommendation r : recs) { log("Recommended: " + r.getItem() + "\n reason: " + r.getExplanation());}

http://openid.sun.com/plamere


Using Aura – Quick Start – Output

Recommended: CAKE reason: geek rock, nerd rock

emo, indie pop, fun, 90s

Recommended: Hot Hot Heat reason: power pop, emo, fun,

indie pop, pop punk

Recommended: Ben Folds Five reason: geek rock, Nerd Rock,

power pop, emo, 90s


Project Aura Quick Start

Project Aura API is very simple• Define Users• Define Items• Add attention data• Get Recommendations

But Aura is not just the API

In Project Aura, we try to solve the problems encountered in traditional recommender systems


Look At All the Things That Can Go WrongIf you likeBritney Spears

Report on Pre-War Intelligence

you might like...

Let's look at some of the issues...


Collaborative FilteringItem similarity based upon the halo of users

mary

sue

bill steve

jim

jack

john

karl

nicole

nigelseth

jeff

vic

boblev

james

jannicole

jen bob

paul

jon

jeff

thrash

jan

jen

jeffvic

james

karl

jonanneseth

paul

mary

suebill

stevejim

jack john

jeffnicole

paul

karl

jack

sue

bill

john


mary

sue

bill steve

jim

jack

john

karl

nicole

nigelseth

jeff

vic

boblev

james

jannicole

jen bob

paul

jon

jeff

thrash

jan

jen

jeffvic

james

karl

jonanneseth

paul

mary

suebill

stevejim

jack john

jeffnicole

paul

karl

jack

sue

bill

john

thrash ???thrash ???A big problem for new items Collaborative Filtering – Cold Start


Cold Start in the WildIf you like Gregorian Chants you might like Green Day


StrangeConnections

More Strange Recommendations


Collaborative Filtering – Cold StartA bigger problem for a new recommender

thrash

thrash


Popularity Bias – the Harry Potter Effect


Current Top 10 at Last.fm


Social Recommenders: Hacking

Courtesy of Bamshad Mobasher

Censored


● Millions of items and users, billions of taste data points

● Recommendation is the secret sauce

● Scaling is hard

● Thousands of items, users, and taste data points

● Data is sparse● Collaborative Filtering

techniques don't work as well

The Scale of the Problem


Agenda

What is recommendation and why do we careWhat is wrong with existing recommendationsHow Project Aura fixes these problemsUsing Project AuraDemonstration


Recommendation for the Rest of Us

Good recommendation needs big data• Millions of users, billions of datapoints• Fine for Amazon, Netflix, iTunes and Google

But what if you don't have big data?• Poor recommendations• No recommendations

We want to give everyone the opportunity to provide good recommendations, regardless of size


Our solution: Augment This...

mary

sue

bill steve

jim

jack

john

karl

nicole

nigelseth

jeff

vic

boblev

james

jannicole

jen bob

paul

jon

jeff

thrash

loud

jan

jen

jeffvic

james

karl

jonanneseth

paul

mary

suebill

stevejim

jack john

jeff

nicole paul

karl

jack

sue

bill

john

thrash ???thrash ???


With Thisthe text aura Similarity based upon

the aura surrounding the musicrock

Indie

Cute

guitar

Drums

90s

FastWeird

twee

Quirky

noise pop

Cute

playful

Drums

80s

FastWeird

Sweet

Quirky

Cuterock

pop

90sFun

metal

rock

Edgyguitar

thrash

90s

FierceWeird

concert

Loudthrash

rock metal

90s

Loud

Weird

thrash

loud metal

death

thrash

rock

Edgygothic

heavy metal

90s

FrenziedWeirdconcert

Loud


Where Does the Text Aura Come From?Crawling the webAnalysis of content

thrash

rock

Edgy

gothic

heavy metal

90s

Weird

concert

Loud

Content Reviews Lyrics Blogs Social Tags Bios Users


Autotagging Music Directly From Audio

Labeled ExamplesUnknown Examples

Machine Learning

Model

Labeled Examples

Windowing

MEL Scale

Decode

FFT

Log

DCT

MFCC

Feature Extraction Training Tagging


Autotagging MusicBohemian Rhapsody

• Classic rock• Favorite artist• Glam Rock• Happy• Psychedelic Rock• 70s• UK • Favourites• England• Prog

Stairway to Heaven• Awesome• Proto-punk• Rock and Roll• Folk Rock• 70s• Genius• Blues Rock• Classic Rock• Great Lyricists• Art Rock

Take Five (Brubeck)• Fusion• Saxophone• Avant-Garde• Trumpet• Jazz Fusion• Instrumental• Jazz• Bossa Nova• Cool• Gentle


Using the Aura to Determining Similarity

Artist similarity based on the tag aura for The BeatlesTop Tags

• classic rock• rock• pop• british• 60s • oldies • psychedelic• alternative• indie • britpop

Distinctive Tags• The Beatles• 60s• liverpool• british• british psychedelia• oldies• britrock• psychedelic• classic rock• Rock and Roll

Similar Artists via Tags• John Lennon• Rolling Stones• Paul McCartney• The Kinks• The Who• Pink Floyd• Queen• The Police• Led Zeppelin• David Bowie


Project Aura Technology Summary

No cold start problemTransparent recommendationsSteerable recommendationsLess susceptible to hacking / shillingImmune to typical biases

How do Project Aura recommendations compare to other systems?


Recommender Performance

Sun Aura 4.023.683.50

Sun CF 3.483.26

Critic 1 2.89Critic 2 2.76

2.592.061.82

Critic 3 1.64Critic 4 1.59Critic 5 1.14

0.890.82

-2.39

Recommender System

Average Rating

Rec Sys ARec Sys B

Rec Sys C

Rec Sys DRec Sys ERec Sys F

Rec Sys GRec Sys HRec Sys I

Web Survey:• 200 Participants• > 10,000 data points

Ranked:• 2 Sun Labs recommenders• 9 Commercial recommenders• 5 Professional music critics

Performance:• 280,000 artists• 7.2 million tags• Find Similar Artist: 200 ms


Agenda

What is recommendation and why do we careWhat is wrong with existing recommendationsHow Project Aura fixes these problemsUsing Project AuraDemonstration


Project Aura Java™ APIs

Open Source – available soon at tastekeeper.comHost it yourself• Advantages

• You keep your data to yourself• You can customize it

• Disadvantages • You have to buy computers• You can't benefit from other big data


Using Aura – Defining Items and Users// Creating and adding a new artistItem artist = aura.newItem(ARTIST, "6fe07", "Weezer" );artist.addField("BIOGRAPHY", "Weezer is a Rock Band..."); aura.putItem(artist);

// Creating and adding a new trackItem track = aura.newItem(TRACK, "39173", "Hash Pipe");track.addField("ARTIST_KEY", artist.getKey()); track.addField("REVIEW", "The opening guitar rif of...");track.addField("LYRICS", "I can't help my feelings...");aura.putItem(track);

// Creating and adding a userUser user = aura.newUser(

"http://openid.sun.com/plamere", "Paul");aura.addUser(user);



Using Aura – Adding Attention Data// Look up the user and the items

User fan = aura.getUser("http://openid.sun.com/plamere");Item weezer = aura.findItemByName("Weezer");Item paris = aura.findItemByName("Paris Hilton");

// Pay attention to the items aura.attend(fan, weezer, Attention.PLAY);aura.attend(fan, weezer, Attention.RATE, Rating.FIVE_STARS);aura.attend(fan, weezer, Attention.TAG, "nerd rock");aura.attend(fan, weezer, Attention.TAG, "seen live");

aura.attend(fan, paris, Attention.SKIP);



Using Aura – Item Similarity

// Look up the userItem weezer = aura.findItemByName("Weezer");

// find 10 artists most similar to WeezerSortedSet<Scored<Item>> items = aura.findSimilar(weezer, 10); // find 10 items most similar based on tagsitems = aura.findSimilar(weezer, TAGS, 10);

// find 10 most similar based on tags, biography and decadeWeightedFields[] tagsBioDecade = {...};items = aura.findSimilar(weezer, tagsBioDecade, 10);


Using Aura – Item Recommendation// find 10 artist to recommend to a userList<Recommendation> recommendations = aura.recommend(user, ARTISTS, 10);for (Recommendation r : recommendations) { log("Recommended " + r.getItem() + " for user " + user + " reason: " + r.getExplanation());}

// find 10 tracks suitable for exercise RecommenderProfile joggingProfile = new RecommenderProfile();joggingProfile.add("BPM", "> 120");joggingProfile.add("PERIOD", "70s");List<Recommendation> recommendations = aura.recommend(user, TRACKS, 10, joggingProfile );


Project Aura Web Services

Open / Free Web Services for Recommendation• You provide taste data• Project Aura provides recommendations

Advantages:• Simple recommendation solution for Web startups• We solve the scaling problems

• Built on top of Project Caroline • Reliable / Scalable• Ready for millions of users / billions of taste datapoints

Disadvantages:• You can't customize it• Your taste data is shared with others


Web-scale RecommendationDataStore

PartitionCluster

ClusterReplicas

DB

SearchIndex

DB

SearchIndex

DB

SearchIndex

CollaborativeFiltering

Recommender

Content-BasedRecommender

HybridRecommender

WebCrawler

WebCrawler

WebCrawler

WebCrawler

WebCrawler

WebCrawler

WebCrawler

WebCrawler

WebCrawler

PartitionCluster

ClusterReplicas

DB

SearchIndex

DB

SearchIndex

DB

SearchIndex

PartitionCluster

ClusterReplicas

DB

SearchIndex

DB

SearchIndex

DB

SearchIndex


Using Aura – Web Services

http://tastekeeper.com/RecommenderService ?apmlURL=http://example.com/lamere.apml &outputFormat=apml &type=artist &num=10 &apmlProfile=MyMusic &recProfile=standard

Simple Web Service:• Input:

• Attention Profile Markup representing taste• Output format• Type of items to recommend• Number of recommendations desired• APML Profile selector• Recommendation Profile selector

• Output:• APML with recommendations embedded

• Example:


Attention Profile Markup Language<apml> <Profile name="MyMusic"> <ExplicitData> <Concepts> <Concept key="Breaking Benjamin" value="1.0"/> <Concept key="Deerhoof" value="0.455"/> <Concept key="The Arcade Fire" value="0.395"/> <Concept key="Radiohead" value="0.384"/>

<Concept key="Björk" value="0.358"/> <Concept key="Weezer" value="0.347"/> <Concept key="The Postal Service" value="0.324"/> <Concept key="Rodrigo y Gabriela" value="0.205"/> <Concept key="Fiona Apple" value="0.201"/> </Concepts> </ExplicitData> </Profile></apml>

Recommendation Input

More info at apml.org

Attention Profile Markup Language<apml> <Profile name="Music-Recommendations"> <ImplicitData>

<Concepts> <Concept key="Elefant" value="0.933"/> <Concept key="Finger Eleven" value="0.917"/> <Concept key="The New Amsterdams" value="0.913"/> <Concept key="Gomez" value="0.902" /> <Concept key="Ocean Colour Scene" value="0.902"/> <Concept key="Three Days Grace" value="0.901" /> <Concept key="Marcy Playground" value="0.900" />  </Concepts> </ImplicitData> </Profile></apml>

Recommendation output


● Use the content of the item● Use the text aura of the items

> Social Tags, Reviews, blog posts, ...

● Use autotagging to populate the aura

● Use taste data to find similar users, items

● Works best with lots of taste data

Content-basedRecommendation

CollaborativeFiltering

● Reduce reliance on big taste data● Reduce cold start and feedback

problems● Provide a way to explain why and

how recommendations were made

Aura Technology Summary


Demo – Project Aura


For More Information

Aura Links• tastekeeper.com – general information about Project Aura

• blogs.tastekeeper.com – a blog recommender based upon Aura• music.tastekeeper.com – a music recommender based upon Aura

• Project Caroline - projectcaroline.netFurther reading• apml.org – attention profile markup language• Team Blogs

• blogs.sun.com/plamere• blogs.sun.com/searchguy

Alternatives• taste.sourceforge.net – collaborative filtering in the Java

programming language


Project Aura: Recommendation for the rest of us

Paul LamereStephen Green

TS-5841

project aura recommendation for the rest of us · • 60s • oldies • psychedelic •...

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