recommendation techn

Practical Machine Learning:

Innovations in Recommendation

Ted Dunning

22 June 2014 Big Data Everywhere Conference Tel Aviv

Who I am

Ted Dunning, Chief Applications Architect, MapR Technologies

Email tdunning@mapr.com tdunning@apache.org

Twitter @Ted_Dunning

Apache Mahout https://mahout.apache.org/

Twitter @ApacheMahout

22 June 2014 Big Data Everywhere Conference #DataIsrael

http://www.wired.com/wiredenterprise/2012/12/mahout/

Recommendation: Widely Used Machine Learning

Example: Open source Apache Mahout used in production

Recommendations

– Data: interactions between people taking action (users) and items • Data used to train recommendation model

– Goal is to suggest additional interactions– Example applications: movie, music or map-based restaurant choices;

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

Google maps: restaurant recommendations

Google maps: tech recommendations

Tutorial Part 1:How recommendation works,or “I want a pony”…

First question:Are you using the right data?

Recommendation

Behavior of a crowd helps us understand what individuals will do

Recommendations

Alice got an apple and a puppyAlice

Recommendations

Charles got a bicycleCharles

Recommendations

Bob Bob got an apple

Recommendations

Bob What else would Bob like?

Recommendations

Bob A puppy!

You get the idea of how recommenders can work…

By the way, like me, Bob also wants a pony…

Recommendations

Charles

Amelia

What if everybody gets a pony?

What else would you recommend for new user Amelia?

Recommendations

Charles

Amelia

If 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 to generate indicators for recommendation– 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

Overview: 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 indicators by identifying 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– ItemSimilarityJob in Apache Mahout uses LLR

Apache Mahout: Overview• Open source Apache project http://mahout.apache.org/• Mahout version is 0.9 released Feb 2014; inc Scala

– Summary 0.9 blog at http://bit.ly/1rirUUL

• Library of scalable algorithms for machine learning– Some run on Apache Hadoop distributions; others do not require Hadoop– Some can be run at small scale– Some are run in parallel; others are sequential

• Includes the following main areas:– Clustering & related techniques– Classification– Recommendation– Mahout Math Library

Log Files

Charles

Log Files

History Matrix: Users x Items

Charles

✔ ✔ ✔

✔ ✔

Co-Occurrence Matrix: Items x Items

2How do you tell which co-occurrences are useful?

2Use LLR test to turn co-occurrence into indicators…

Co-occurrence Binary Matrix

Which one is the anomalous co-occurrence?

A not A

B 13 1000

not B 1000 100,000

A not A

not B 0 10,000

A not A

B 10 0

not B 0 100,000

A not A

not B 0 2

Which one is the anomalous co-occurrence?

A not A

B 13 1000

not B 1000 100,000

A not A

not B 0 10,000

A not A

B 10 0

not B 0 100,000

A not A

not B 0 20.90 1.95

4.52 14.3

2Recap:Use LLR test to turn co-occurrence into indicators

Indicator Matrix: Anomalous Co-Occurrence

Result:Each marked row shows the indicators of what to recommend…

✔✔

Indicator Matrix: Anomalous Co-Occurrence

✔✔

Why not pony + other item?

How will you deliver recommendations to users?

Seeking Simplicity

Innovation: Exploit search technology to deploy your recommendation system

But first, a look at how search works…

Apache Solr/Apache Lucene

• Apache Solr/Lucene is an open-source powerful search engine used for flexible, heavily indexed queries including data such as– Full text, geographical data, statistically weighted data

• Lucene – Provides core retrieval– Is a low-level library

• Solr – Is a web-based wrapper around Lucene – Is easy to integrate because you talk to it via a web-interface

• URL http://host machine:8888

LucidWorks

• Enterprise platform and collection of applications based on Apache Solr/Lucene– Wrapper around Solr– A free version ships with MapR

• LucidWorks leaves Solr exposed but makes Solr administration much easier, which in turn makes it easier to use Lucene

• URL http://host machine:8989

LucidWorks

Query Response

Lucene

Query Response

Data Source

Relationship of Solr /Lucene/LucidWorks

Other Options: Elastic Search

• Apache Lucene library at the heart of several approaches– It can also be used on its own

• Elastic Search is a different web interface for Lucene– Real time search and analytics– Open source (not Apache)– Big advantage is less accumulated cruft

http://www.elasticsearch.com/

What is a Document?

• Data is stored in collections made up of documents• Documents contain fields that can be

– Indexed • makes field searchable; don’t have to index all

– Stored• If you want Solr to return content, have Solr store content• Not all data of interest must be stored: can access via stored URL (good for very

large data set)

– Multi-value • Body field can contain more than one type of data

– Facetted• A way to refine a search or use for statistics• Example: data for country: Could return facetted as “37 from US, 23 UK, 7 Japan”

Fields and How to Set Them Up

• Lucene is mostly used for text– Text has to be tokenized

• Also supports other types– Long, string, keywords, comma separated

• Fields properties such as stored, indexed, faceted can also be defined

• Defaults aren’t usually so great

Example of Facetted Search

The indexed fields “Area” and “Gender” have been facetted to provide counts for the results

Field-specific Searches Using Lucene Syntax

• Documents often have title, author, keywords and body text• General syntax is

field:(term1 term2)

• Alternativesfield:(term1 OR term2) field:(“term1 term2” ~ 5) field:(term1 AND term2)

• Default field, default interpretations work very well for text

Send Data to Solr (not LucidWorks)

• LucidWorks has lots of spiders that can use file extension or mime type to trigger certain file parsers– Works best with web-ish sources and mime types– Also includes MapR and MapR high volume indexers

• More common at modest volumes or for updates to use JSON format

{"id":"book_314", "title":{"set":"The Call of the Wild"}}

• Use REST interface to send update filescurl http://localhost:8983/solr/update \

-H 'Content-type:application/json' --data-binary @file.json

Back to recommendation: How do you abuse search to make recommendation easy?

Collection of Documents: Insert Meta-Data

Search Technology

Item meta-data

Document for “puppy” id: t4

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

Ingest easily via NFS

From Indicator Matrix to New Indicator Field

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

indicators: (t1)

Solr document for “puppy”

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

Let’s look at a real example: we built a music recommender

User activity: Listens to classic jazz hit “Take the A Train”

System delivers recommendations based on activity

Let’s look inside the music recommender…

Music Meta Data for Search Document Collections

• MusicBrainz data

• Data includes Artist ID, MusicBrainz ID, Name, Group/Person, From (geo locations) and Gender as seen in this sample

Sample User Behavior Histories: Music Log Files

13 START 10113 2182654281

23 BEACON 10113 218265 428124 START 10113 796006

1193502834 BEACON 10113 7960061193502844 BEACON 10113 7960061193502854 BEACON 10113 7960061193502864 BEACON 10113 7960061193502874 BEACON 10113 7960061193502884 BEACON 10113 7960061193502894 BEACON 10113 79600611935028104 BEACON 10113 79600611935028109 FINISH 10113 79600611935028111 START 10113 589999

12011972121 BEACON 10113 58999912011972

Event type

User ID

Artist ID

Track ID

Sample Music Log Files

Artist ID for jazz musician Duke Ellington

What has user 119 done here in the highlighted lines?

Internals of a Recommendation Engine

id 1710mbid 592a3b6d-c42b-4567-99c9-ecf63bd66499name Chuck Berryarea United Statesgender Maleindicator_artists 386685,875994,637954,3418,1344,789739,1460, …

id 541902mbid 983d4f8f-473e-4091-8394-415c105c4656name Charlie Winstonarea United Kingdomgender Noneindicator_artists 997727,815,830794,59588,900,2591,1344,696268, …

Lucene Documents for Music RecommendationNotice that data from indicator matrix of trained Mahout recommender model has been added to indicator field in documents of the artists collection

Offline Analysis

Analysis Using Mahout

Users History Log Files Indicators

Search Technology

Item Meta-Data

Log FilesMahout Analysis

Search Technology

Item Meta-Data

Ingest easily via NFS

MapR Cluster

via NFS Python

Use Python directly via NFS

Web TierRecommendations

New User History

Real-time recommendations using MapR data platform

A Quick Simplification• Users who do h

• Also do

User-centric recommendations

Item-centric recommendations

Architectural Advantage

With the first design, you have to do the real-time computation first (in parenthesis). No way to pre-compute. Less efficient, less fast.

With the second design, you can pre-compute offline (overnight) things that change slowly. Only the smaller computation for new user vector (h) is done in real-time, so response is very fast.

Tutorial Part 2:How to make recommendation better

Going Further: Multi-Modal Recommendation

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 de 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?

Symmetry Gives Cross Recommentations

Conventional recommendations with off-line learning

Cross recommendations

things

thingtype 1

thingtype 2

Bonus Round:

When worse is better

The Real Issues After First Production

• Exploration• Diversity• Speed

• Not the last fraction of a percent

Result Dithering

• Dithering is used to re-order recommendation results – Re-ordering is done randomly

• Dithering is guaranteed to make off-line performance worse

• Dithering also has a near perfect record of making actual performance much better

Result Dithering

• Dithering is used to re-order recommendation results – Re-ordering is done randomly

• Dithering is guaranteed to make off-line performance worse

• Dithering also has a near perfect record of making actual performance much better

“Made more difference than any other change”

Why Dithering Works

Real-time recommender

Overnight training

Log Files

Why Use Dithering?

Simple Dithering Algorithm

• Synthetic score from log rank plus Gaussian

• Pick noise scale to provide desired level of mixing

• Typically

• Also… use floor(t/T) as seed

Example … ε = 2

Lesson:Exploration is good

Thank you

Ted Dunning, Chief Applications Architect, MapR Technologies

Email tdunning@mapr.com tdunning@apache.org

Twitter @Ted_Dunning

Apache Mahout https://mahout.apache.org/

Twitter @ApacheMahout

22 June 2014 Big Data Everywhere Conference #DataIsrael

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