Building multi-modal recommendation engines using search engines

1©MapR Technologies 2013- Confidential

Introduction to MahoutAnd How To Build a Recommender


Topic For Today

What is recommendation? What makes it different? What is multi-model recommendation? How can I build it using common household items?


Oh … Also This

Detailed break-down of a live machine learning system running with Mahout on MapR

With code examples


I may have to summarize


I may have to summarize

just a bit


Part 1:5 minutes of background


Part 2:5 minutes: I want a pony



Part 1:5 minutes of background


What Does Machine Learning Look Like?


What Does Machine Learning Look Like?

O(κ k d + k3 d) = O(k2 d log n + k3 d) for small k, high qualityO(κ d log k) or O(d log κ log k) for larger k, looser quality

But tonight we’re going to show you how to keep it simple yet powerful…


Recommendations as Machine Learning

Recommendation: – Involves observation of interactions between people taking action (users)

and items for input data to the recommender model– Goal is to suggest additional appropriate or desirable interactions– Applications include: movie, music or map-based restaurant choices;

suggesting sale items for e-stores or via cash-register receipts




Part 2:How recommenders work

(I still want a pony)


Recommendations

Recap:Behavior of a crowd helps us understand what individuals will do


Recommendations

Alice got an apple and a puppy

Charles got a bicycle

Alice

Charles


Recommendations

Alice got an apple and a puppy

Charles got a bicycle

Bob got an apple

Alice

Bob

Charles


Recommendations

What else would Bob like??

Alice

Bob

Charles


Recommendations

A puppy, of course!

Alice

Bob

Charles


You get the idea of how recommenders work… (By the way, like me, Bob also wants a pony)


Recommendations

What if everybody gets a pony?

?

Alice

Bob

Charles

Amelia What else would you recommend for Amelia?


Recommendations

?

Alice

Bob

Charles

AmeliaIf everybody gets a pony, it’s not a very good indicator of what to else predict...


Problems with Raw Co-occurrence

Very popular items co-occur with everything (or why it’s not very helpful to know that everybody wants a pony…)– Examples: Welcome document; Elevator music

Very widespread occurrence is not interesting as a way to generate indicators – Unless you want to offer an item that is constantly desired, such as razor

blades (or ponies)

What we want is anomalous co-occurrence– This is the source of interesting indicators of preference on which to

base recommendation


Get Useful Indicators from Behaviors

1. Use log files to build history matrix of users x items– Remember: this history of interactions will be sparse compared to all

potential combinations

2. Transform to a co-occurrence matrix of items x items

3. Look for useful co-occurrence by looking for anomalous co-occurrences to make an indicator matrix

– Log Likelihood Ratio (LLR) can be helpful to judge which co-occurrences can with confidence be used as indicators of preference

– RowSimilarityJob in Apache Mahout uses LLR


Log Files

Alice

Bob

Charles

Alice

Bob

Charles

Alice


Log Files

u1

u3

u2

u1

u3

u2

u1

t1

t4

t3

t2

t3

t3

t1


Log Files and Dimensions

u1

u3

u2

u1

u3

u2

u1

t1

t4

t3

t2

t3

t3

t1

t1

t2

t3

t4

Things

u1 Alice

BobCharles

u3u2

Users


History Matrix: Users by Items

Alice

Bob

Charles

✔ ✔ ✔

✔ ✔

✔ ✔


Co-occurrence Matrix: Items by Items

-

1 2

1 1

1

1

2 1

How do you tell which co-occurrences are useful?.

00

0 0


Co-occurrence Matrix: Items by Items

-

1 2

1 1

1

1

2 1

Use LLR test to turn co-occurrence into indicators…

00

0 0


Co-occurrence Binary Matrix

1

1not

not

1


Spot the Anomaly

A not A

B 13 1000

not B 1000 100,000

A not A

B 1 0

not B 0 2

A not A

B 1 0

not B 0 10,000

A not A

B 10 0

not B 0 100,000

What conclusion do you draw from each situation?


Spot the Anomaly

Root LLR is roughly like standard deviations In Apache Mahout, RowSimilarityJob uses LLR

A not A

B 13 1000

not B 1000 100,000

A not A

B 1 0

not B 0 2

A not A

B 1 0

not B 0 10,000

A not A

B 10 0

not B 0 100,000

0.90 1.95

4.52 14.3

What conclusion do you draw from each situation?


Co-occurrence Matrix

-

1 2

1 1

1

1

2 1

Recap: Use LLR test to turn co-occurrence into indicators

00

0 0


Indicator Matrix: Anomalous Co-Occurrence

✔✔

Result: The marked row will be added to the indicator field in the item document…


Indicator Matrix

✔

id: t4title: puppydesc: The sweetest little puppy ever.keywords: puppy, dog, pet

indicators: (t1)

That one row from indicator matrix becomes the indicator field in the Solr document used to deploy the recommendation engine.

Note: data for the indicator field is added directly to meta-data for a document in Solr index. You don’t need to create a separate index for the indicators.


Internals of the Recommender Engine

38


Internals of the Recommender Engine

39


Looking Inside LucidWorks

What to recommend if new user listened to 2122: Fats Domino & 303: Beatles?

Recommendation is “1710 : Chuck Berry”

40

Real-time recommendation query and results: Evaluation


Search-based Recommendations

Sample document– Merchant Id– Field for text description– Phone– Address– Location




– Indicator merchant id’s– Indicator industry (SIC) id’s– Indicator offers– Indicator text– Local top40





Sample query– Current location– Recent merchant descriptions– Recent merchant id’s– Recent SIC codes– Recent accepted offers– Local top40





Sample query– Current location– Recent merchant descriptions– Recent merchant id’s– Recent SIC codes– Recent accepted offers– Local top40

Original data and meta-data

Derived from cooccurrence and cross-occurrence analysis

Recommendation query


For example

Users enter queries (A)– (actor = user, item=query)

Users view videos (B)– (actor = user, item=video)

ATA gives query recommendation– “did you mean to ask for”

BTB gives video recommendation– “you might like these videos”


The punch-line

BTA recommends videos in response to a query– (isn’t that a search engine?)– (not quite, it doesn’t look at content or meta-data)


Real-life example

Query: “Paco de Lucia” Conventional meta-data search results:– “hombres del paco” times 400– not much else

Recommendation based search:– Flamenco guitar and dancers– Spanish and classical guitar– Van Halen doing a classical/flamenco riff


Real-life example


Hypothetical Example

Want a navigational ontology? Just put labels on a web page with traffic– This gives A = users x label clicks

Remember viewing history– This gives B = users x items

Cross recommend– B’A = label to item mapping

After several users click, results are whatever users think they should be


Nice. But we can do better?


A Quick Simplification

Users who do h (a vector of things a user has done)

Also do r

User-centric recommendations(transpose translates back to things)

Item-centric recommendations(change the order of operations)

A translates things into users


Symmetry Gives Cross Recommentations

Conventional recommendations with off-line learning

Cross recommendations


users

things


users

thingtype 1

thingtype 2



Part 3:What about that worked example?


http://bit.ly/18vbbaT


SolRIndexerSolR

IndexerSolrindexing

Cooccurrence(Mahout)

Item meta-data

Indexshards

Complete history

Analyze with Map-Reduce


SolRIndexerSolR

IndexerSolrsearchWeb tier

Item meta-data

Indexshards

User history

Deploy with Conventional Search System


Me, Us

Ted Dunning, Chief Application Architect, MapRCommitter PMC member, Mahout, Zookeeper, DrillBought the beer at the first HUG

MapRDistributes more open source components for HadoopAdds major technology for performance, HA, industry standard API’s

InfoHash tag - #maprSee also - @ApacheMahout @ApacheDrill

@ted_dunning and @mapR

Technology

Building multi-modal recommendation engines using search engines