sanoma search use cases

Sanoma Search Use CasesSander Kieft@skieft

About me@skieft

Manager Core Services at Sanoma

Responsible for all common services, including the Big Data

platform

Work:

– Centralized services

– Data platform

– Search

Like:

– Work

– Water(sports)

– Whiskey

– Tinkering: Arduino, Raspberry PI, soldering stuff24 April 20152

Sanoma, Publishing and Learning company

2+1002 Finnish newspapers

Over 100 magazines

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5TV channels in Finland

and The Netherlands

200+Websites

100Mobile applications on

various mobile platforms

Sanoma = Donald Duck

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Use cases

Full text search

Use cases

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Full Text Search

Photo credits: Igal Koshevoy - https://www.flickr.com/photos/igalko/6345215839/

Facetted search

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Full text search

Facetted search

Guided search

Use cases

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Guided search

Photo credits: http://www.flickr.com/photos/emyanmei/8223998414/

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Source: ThesisDefense - Dirk Guijt

Startpagina.nl Search

Content

Source: ThesisDefense - Dirk Guijt

Startpagina.nl Search

ContentTags

#vakantie

#arke

#vakantie arke

#arke vakantie

#arke stedentrip

#stedentrip arke

…

#arkefly

Source: ThesisDefense - Dirk Guijt

Startpagina.nl Search

ContentTags

#vakantie

#arke

#vakantie arke

#arke vakantie

#arke stedentrip

#stedentrip arke

…

#arkefly

Query: vakantie arke

Source: ThesisDefense - Dirk Guijt

Startpagina.nl Search

ContentTags

#vakantie

#arke

#vakantie arke

#arke vakantie

#arke stedentrip

#stedentrip arke

…

#arkefly

Query: vakantie arke

Source: ThesisDefense - Dirk Guijt

Startpagina.nl Search

ContentTags

#vakantie

#arke

#vakantie arke

#arke vakantie

#arke stedentrip

#stedentrip arke

…

#arkefly

?

Query: vakantie arke stedentrip

Thesis Defense - Dirk Guijt

Term mismatch

“vakantie arke stedentrip” “arke stedentrip”

Query-Flow-Graph

User ID Date / Time Query

User A 02-12-2014 10:30:15 owls

User A 02-12-2014 10:30:23 snow owls

User A 02-12-2014 10:30:46 snow owls food

User A 02-12-2014 10:31:03 owls food

User B 02-12-2014 13:21:34 lemon

User B 02-12-2014 13:22:02 lemon trees

User B 02-12-2014 13:22:12 lemon cove

User B 02-12-2014 16:53:01 owls

User B 02-12-2014 16:53:53 forest owls

Source: ThesisDefense - Dirk Guijt

Model as a Graph

snow owls

snow owls food

owls food

owls

lemon trees

lemon cove

lemon

forest owls

User ID Date / Time Query

User A 02-12-2014 10:30:15 owls

User A 02-12-2014 10:30:23 snow owls

User A 02-12-2014 10:30:46 snow owls food

User A 02-12-2014 10:31:03 owls food

User B 02-12-2014 13:21:34 lemon

User B 02-12-2014 13:22:02 lemon trees

User B 02-12-2014 13:22:12 lemon cove

User B 02-12-2014 16:53:01 owls

User B 02-12-2014 16:53:53 forest owls

Source: ThesisDefense - Dirk Guijt

Model as a Graph

snow owls

snow owls food

owls food

owls

lemon trees

lemon cove

lemon

forest owls

5

3

1

2

6

5

owls snow owls (S) specialization

snow owls owls (G) generalization

olws owls (C) same query / error correction

snow owls forest owls (P) parallel move / equivalent rephrase

Source: ThesisDefense - Dirk Guijt

Using Query-Log based Collective Intelligence to Generate Query Suggestions for Tagged Content Search (paper)15th International Conference on Web Engineering (ICWE 2015): http://icwe2015.webengineering.org

Query Reformulation Types

Examples

Source: ThesisDefense - Dirk Guijt

Using Query-Log based Collective Intelligence to Generate Query Suggestions for Tagged Content Search (paper)15th

International Conference on Web Engineering (ICWE 2015): http://icwe2015.webengineering.org

Full text search

Facetted search

Guided search

Content repository

Use cases

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Monolithic vs integrated

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Two approaches

Master

Content

*

*

Content

Master

Content

*

*

MyJour

Item Based Framework

….CMS

Architecture Content Platform

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Content Platform Core

Search

Solr

Blob

Storage

(S3 & MT)

Article

storage

MongoDB

Analyse

CMS

CMS

Editorial

reuse-interface

ePub

Digital

Template

system

WoodWing

Content

Portal

Feeds

Noma

Viva

PDF Based Framework

….

HomeDeco

Sources Services Solutions Products

??

??

??

??

eLinea

Blendle

Google Currents

LINDA. nieuws

NU.nl search

NLPNatural Language Processing

Understanding a language is hard, really hard

Photo credits: Celines Photographer - http://www.flickr.com/photos/celinesphotographer/2295348530/

Ambiguous

Photo credits: M Hatrey - http://www.flickr.com/photos/mhatrey/6968211400/

I made her duck.

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Photo credits: Ulteriore Picure - http://www.flickr.com/photos/ulteriorepicure/200767137/

Photo credits: Atomic Seed - http://www.flickr.com/photos/atomic_seed/6824087444/

Photo credits: Ulteriore Picure - http://www.flickr.com/photos/ulteriorepicure/200767137/

Photo credits: Pintoy - http://www.flickr.com/photos/pintoys/6155690814/

Photo credits: KyanosAum - http://www.flickr.com/photos/kyanos_aum/3926971954/

Photo credits: Super Hua - http://www.flickr.com/photos/superhua/286479024/

Photo credits: Glim Eend - http://www.flickr.com/photos/glimeend/5075731300/

Wrong

Photo credits: Learnscope - http://www.flickr.com/photos/learnscope/5536614305/

Recursion

Photo credits: Wikipedia - http://en.wikipedia.org/wiki/File:Droste.jpg

Creative Tools - http://www.flickr.com/photos/creative_tools/4353860378/

Multi lingual

Ambiguity

Creative

Multi lingual

Wrong

..and many more

Understanding language is hard

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..but we don’t need to fully understand language to take care of it.

Tagging

Quote detection

Sentiment analysis

Topic detection

Named Entity Recognization

What did we use to enhance our index?

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Tagging

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Spectators outside the White House received a rare treat this morning

when they witnessed First Lady Michelle Obama on the South Lawn

going for a stroll with the family’s pet rhinoceros, Chauncey. “Owning a

rhino is a lot of work, but all of the Obamas—and especially Michelle—

really love Chauncey,” said White House spokesperson Sam

Davidson of the 3,000-pound eastern black rhinoceros the family

adopted in December after Barack Obama’s reelection promise to

“finally get Sasha and Malia that rhino they’ve been wanting.”

Source: http://www.theonion.com/articles/michelle-obama-seen-outside-walking-family-rhinoce,32851/

TF/IDF

Latent semantic indexing

Tagging

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TF/IDF

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TF-IDFTerm Frequency-Inverse Document Frequency

How often does the search

term occur in the text

How many words are in

the entire text

5/24 = 0,213/12 = 0,25

More relevant

NumPy, for SVD

Latent semantic indexing

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?

Many interesting things about text are longer than one word

– bigram: a sequence of two tokens

– collocation: a bigram that seems to be more than the sum of its parts

When is a bigram interesting?

#(vice president) #(vice)

#(president)

#(first lady) #(first)

#(lady)

Statistics beyond single words

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Quotes

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Spectators outside the White House received a rare treat this morning

when they witnessed First Lady Michelle Obama on the South Lawn

going for a stroll with the family’s pet rhinoceros, Chauncey. “Owning a

rhino is a lot of work, but all of the Obamas—and especially Michelle—

really love Chauncey,” said White House spokesperson Sam Davidson

of the 3,000-pound eastern black rhinoceros the family adopted in

December after Barack Obama’s reelection promise to “finally get Sasha

and Malia that rhino they’ve been wanting.”

Source: http://www.theonion.com/articles/michelle-obama-seen-outside-walking-family-rhinoce,32851/

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TF/IDF

Average per sentence

Quotes

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Classification

Photo credits: biodivlibrary- http://www.flickr.com/photos/biodivlibrary/6989150578/

Distinguish things from other things based on examples

Using supervised learning

Applications:

– Sentiment

– Spam filtering

– Topic detection

– Language detection

Classification

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Classification > Sentiment

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Bayesian model trained on Kieskeurig.nl review data

Calculates chance of being a positive of negative article

Classification > Sentiment

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Classification > Topic detection

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Food and drinks BeautyRelationships and

sexScience

K Nearest Neighbor

Solr related search

Classification > Topic detection

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?

Named Entities

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Spectators outside the White House received a rare treat this morning

when they witnessed First Lady Michelle Obama on the South Lawn

going for a stroll with the family’s pet rhinoceros, Chauncey. “Owning a

rhino is a lot of work, but all of the Obamas—and especially Michelle—

really love Chauncey,” said White House spokesperson Sam Davidson

of the 3,000-pound eastern black rhinoceros the family adopted in

December after Barack Obama’s reelection promise to “finally get

Sasha and Malia that rhino they’ve been wanting.”

Source: http://www.theonion.com/articles/michelle-obama-seen-outside-walking-family-rhinoce,32851/

Locations

Persons

This can get you the persons, organizations and locations based from the sentence structure

Other types of entities can be trained as well, e.g. events, ingredients

Bayesian model

Features:

– Capitalization “Donald Duck”

– Part of speech tag “Did you meet Donald Duck last week?”

– Gazetteer of common names “Donald”

Named Entities

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It was revealed in December 2006 that Michael Jackson "is and has been suffering for at least a

decade from Parkinson's Disease."[10] He also suffered from diabetes. Michael Jackson died of a heart

attack in his home the morning of 30 August 2007 at the age of …

Named Entities++

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?

It was revealed in December 2006 that Michael Jackson "is and has been suffering for at least a

decade from Parkinson's Disease."[10] He also suffered from diabetes. Michael Jackson died of a heart

attack in his home the morning of 30 August 2007 at the age of …

Named Entities++

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{'December': 3,'Parkinson’s Disease': 1,'died': 4,'heart attack': 2}

{'December': 2,'Thriller': 1,'Neverland': 2,'heart attack': 2}

{'December': 3,'Parkinson’s Disease': 1,'Writer': 4,'heart attack': 2}

Storing it in the search index

Use it for faceted search

Use it for boosting

Use it as a poor-man knowledge base/graph

What do we do with this additional info?

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Images & Video

Photo credits: Danila- http://www.flickr.com/photos/58372504@N05/6996542804/

Face detection

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Not face recognition!

OpenCV

Viola Jones

Cascading haar classifier

Currently identify:

– Portrait shots

– Faces

– Group shots

Side project:

– Content sensitive cropping

Color detection

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Image Histogram

Manual mapping

Improving image search

Photo credits: Camera Wiki- http://www.flickr.com/photos/camerawiki/5610384297/

EUVision, UvA spin off

Acquired by Qualcomm end of ‘14

Image and video classification

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High level architecture

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Content Library

NERSentim

ent

Search

index

API

Loader

SolrMongo

DB

Image

recon.

Analyse

Pipeline

Python/Django, with Django Rest framework

MongoDB

Solr

Celery

Libraries:

– nltk

– NumPy & SciPi

– OpenCV

– ssdeep

– EUVision

Technology

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Full text search

Facetted search

Guided search

Content repository

Analytics

Use cases

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Search for analytics

Keyword search can be combined with

advanced forms of ranking the results

Most of the fields go to an index

Facets can be used for analytics

Ranker can be replaced with custom logic

Products:

– Solr

– ElasticSearch

– Marklogic

Use cases:

– Content Search

– Analytics / Faceted

– Percolation

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ELK – ElasticSearch, LogStash & Kibana

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Full text search

Facetted search

Guided search

Content repository

Analytics

Adserving

Use cases

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Search

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Content

Q Σ Result ranking

Search too

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Content

t

Σ Result ranking

User

Search too

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Ads

Page

Σ Result ranking

User

Search too

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Ads

Page

Σ Result ranking

User

History

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Open Projects

OL2R

– Implement

Content Library

– Additional semantic modules

– Impact Sematic Enrichments

Content Library

– Improve Ranking

Guided Search

– Evaluations

– User History

Image search

Analytics

Knowledge Base

– Build for Content Library and Guided Search

Multi lingual search

– NL, EN and FI

Probalistic search

– Product searches

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Peter Dutton - https://www.flickr.com/photos/joeshlabotnik/14040532589/

Camera Wiki - http://www.flickr.com/photos/camerawiki/5610384297/

Danila - http://www.flickr.com/photos/58372504@N05/6996542804/

Biodivlibrary - http://www.flickr.com/photos/biodivlibrary/6989150578/

Wikipedia - http://en.wikipedia.org/wiki/File:Droste.jpg

Creative Tools - http://www.flickr.com/photos/creative_tools/4353860378/

Learnscope - http://www.flickr.com/photos/learnscope/5536614305/

Super Hua - http://www.flickr.com/photos/superhua/286479024/

Kyanos Aum - http://www.flickr.com/photos/kyanos_aum/3926971954/

Pintoy - http://www.flickr.com/photos/pintoys/6155690814/

Ulteriore Picure - http://www.flickr.com/photos/ulteriorepicure/200767137/

Atomic Seed - http://www.flickr.com/photos/atomic_seed/6824087444/

M Hatrey - http://www.flickr.com/photos/mhatrey/6968211400/

Celines Photographer - http://www.flickr.com/photos/celinesphotographer/2295348530/

Irene Mei - http://www.flickr.com/photos/emyanmei/8223998414/

Igal Koshevoy - https://www.flickr.com/photos/igalko/6345215839/

Photo creditsThanks to all Photographers!

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