Intelligent Information Discovery: Machine Driven Search - Liferay DEVCON 2017, Amsterdam,...

André de Oliveira | @arbocombr

Intelligent Information DiscoveryMachine Driven Search

André de OliveiraSearch Engineering Lead, Liferay Inc.


The explosion of content


What is yourinformation discovery strategy?


▪ 1st Generation: BrowseWhat is your discovery strategy?


▪ Customer "Journey" - with a handrail...▪ Breadcrumbs...▪ Product catalog with category tree...▪ Early "search" with SQL queries...

- Click oriented information discovery -(Still the strategy of many legacy applications)

1st Generation: Browsing for content

Discovery gateway:"The Navigation Menu"


▪ 1st Generation: Browse▪ 2nd Generation: Search

What is your discovery strategy?


▪ Search engines; full text search; facets▪ Customer Journey: freedom from "navigation"▪ User → Application: trial and error until found▪ Results "in the now" - missed new content

- Keyword oriented information discovery -(Strategy of choice for modern applications)

2nd Generation: Searching for content

Discovery gateway:"The Search Bar"


▪ 1st Generation: Browse▪ 2nd Generation: Search▪ Next Generation: Predict

What is your discovery strategy?


▪ Every user action is an indication of interest▪ Searches + Browsing paths + Purchase history...

▪ New content, matching interest? Show it now▪ Application → User: find for you - "search-less"▪ Customer Journey continually improves itself

- Interest oriented information discovery -Dominant discovery strategy of the future.

Next Generation: Predicting relevant content

Discovery gateway:"Everywhere"


Intelligentinformation discovery


▪ Beyond the database▪ More than filters: scores▪ Information retrieval▪ Full text queries▪ Ranking and relevance algorithms

It all starts with Search...

How to use a search engine to predict relevant content?


Input = KeywordsOutput = Scored predictions

Predicting with a search engine

Using... Calculate score of...

Autocomplete User input against document titles

Did you mean…? User input against spellcheck dictionary

Suggest as you type User input against popular queries

More like this Whole result documents against other documents

Percolators Whole new documents against predefined queries


▪ Data Science and Machine Learning▪ Neural networks can be trained to make predictions

▪ A scored guess that best matches prior known results▪ Universal, reusable mathematical algorithms

▪ Regression, Classification, Clustering...▪ A trained neural network is like an API

▪ As long as you can feed it numerical input

Input = NumbersOutput = Scored predictions

Rise of the machines


Modeling your input universe as meaningful numbers

The essential challenge in Machine Learning

Picture?

Voice?

Search query?

User indication of

interest?

Scored prediction

Scored prediction


Pictures ⇒ Pixels ⇒ Numbers

Image classification


Sound waves ⇒ Frequencies and amplitudes ⇒ NumbersSpeech recognition


Facial landmarks ⇒ Measurements ⇒ NumbersFace detection


Universe ⇒ Numerical model ⇒ Algorithms ⇒ Scored predictions

Predicting with Deep Learning: Confidence

Picture?

Voice?

Search query?

User indication of

interest?

Scored prediction

Scored prediction

#LRDEVCON 2017 Highlight

"Machine Learning and DXP - The best of 2 worlds"Carlos Hernandez & Filipe Afonso,

Senior Consultants, Liferay


▪ A similarity is a scoring / ranking model▪ Leverage information retrieval algorithms

▪ BM25, ▪ divergence from randomness, ▪ divergence from independence, ▪ information based...

▪ Can be mapped per field - fine tuning▪ Some models suit shorter fields better (BM25)

▪ Elasticsearch Similarity Module

Predicting with Search: Similarity

"value" : 2.7051764, "description" : "score(doc=0,freq=1.0), product of:", "details" : [ { "value" : 0.66422296, "description" : "queryWeight, product of:", "details" : [ { "value" : 4.0726933, "description" : "idf(docFreq=4, maxDocs=108)", "details" : [ ] }, { "value" : 0.16309182, "description" : "queryNorm", "details" : [ ] } ] }, { "value" : 4.0726933, "description" : "fieldWeight in 0, product of:", "details" : [ { "value" : 1.0, "description" : "tf(freq=1.0), with freq of:", "details" : [ { "value" : 1.0, "description" : "termFreq=1.0", "details" : [ ] } ] }, { "value" : 4.0726933, "description" : "idf(docFreq=4, maxDocs=108)", "details" : [ ] }, { "value" : 1.0, "description" : "fieldNorm(doc=0)", "details" : [ ]

Elasticsearch Explain API


▪ Numerical representation of a search▪ TF–IDF = Term Frequency–Inverse Document Frequency▪ Tokenize the text▪ Create appropriate term vectors▪ Prepare a TF–IDF matrix

Textual analysis

Term Term Countthis 1is 1a 2sample 1

Document 1

Term Term Countthis 1is 1another 2example 3

Document 2


TF–IDF (Term Frequency–Inverse Document Frequency)

In question form... Score increases...

Term Frequency How often does a term appear in a field? + When the term pops up a lot of times along the text

Inverse Document Frequency

How rare is the term in the whole index? + When the term is found in this document and not many others

Field-length Norm How short is the field where the term is? + When there isn't much else in the same field (like, a title)

https://www.elastic.co/guide/en/elasticsearch/guide/current/scoring-theory.html


▪ Sanitize out "stopwords"▪ Irrelevant words and phrases

▪ Spell check aggressively▪ "Search for … instead"

▪ Predict non text fields as well▪ Serial numbers (and how to analyze them)

▪ More than one way to say the same thing▪ Synonyms; alternate spellings; separate v. combined words

▪ Contextualize (every application is different)▪ The "adwords" effect - search for this, always show that

Improving textual analysis for better predictions


Machine driven search


▪ Predict relatable searches▪ Given a user initiated search,▪ in a universe of searches from all customers,▪ classify by similar interest group,▪ suggest and push predicted relatable results

▪ A user search is an indication of interest in itself▪ Algorithms / recommender systems▪ Smart content delivery

Machine Driven Search


▪ Autocomplete: User input → Document titles▪ Suggest as you type: User input → Popular queries▪ Base case is quickly exhausted; be creative▪ Primary field (e.g. title)

▪ Combine multiple queries for score - match, prefix, phrase▪ Multiple fields

▪ More matches, and may be what they're really looking for▪ Multiple target entities

▪ Why limit to one kind of content?▪ Organize and render drop list accordingly

Predict with Search: Instant results in search bar


▪ Find content similar to a previous existing document▪ and/or additional user input ▪ and/or arbitrary content

▪ MLT on specific target fields▪ “title”, “description”, “content”

▪ TF–IDF is key▪ Input is analyzed same as target fields▪ TF–IDF is calculated for all terms▪ Top terms with highest TF–IDF are selected▪ Combined "OR" query with top terms only

Predict with Search: More Like This


▪ “Classify” content from users, given any number of rules▪ Rules are registered as “percolator queries”

▪ Submit incoming documents to rule set▪ “Would this doc match this query?”

▪ Response indicates rules that matched▪ Content can then be “classified” accordingly

Predict with Search: Percolate


"Going in reverse to move forward: How reverse querying gives you fully automated publishing"

Jan Verweij, Sales Engineer, Liferay


▪ Store “successful” queries for users▪ Definition of “successful” according to your Customer Journey▪ Indication of interest

▪ Cluster directly visited content with successful result hits▪ To further refine content relevancy

▪ Cluster successful queries from users with a similar journey▪ “Users belonging to customer profile X also search for Y”▪ Data Science and Single Customer View

Predict with Search: Recommended for YOU


"Single Customer View Demystified"Jonathan Lee, Product Manager, Liferay


▪ Discovery gateway: everywhere▪ Front page (Since your last visit)▪ Search bar (As you type)▪ Search results (Also looked for)▪ Content view (You may also like)▪ Push notifications (Never miss another)

▪ Match new content ▪ previous "successful" actions from user

▪ Anticipate and influence▪ Ever-improving Intelligent Information Discovery

Smart Content Delivery


What’s next for Liferay Search


Elasticsearch 6


▪ Rethinking Indexer architecture for extensibility▪ Composition over inheritance▪ Small, single-purpose components▪ New extension points▪ Reusable▪ Easier to test

Modular Search infrastructure


▪ Building custom Search experiences▪ Reusable and flexible▪ Search Page Templates▪ Search Bar▪ Search Results▪ Multi Selection Facets▪ Insights▪ Map▪ … and your own Search-aware Portlets

New Search Components


▪ Powered by Search, end to end▪ Improved User Experience▪ Data driven - and faster than the database▪ Ready for Intelligent Information Discovery

Liferay Commerce


"Liferay Commerce: A Preview of Our Upcoming Features"Marco Leo, Software Architect, Liferay


Intelligent Information Discovery

LiferayDXP

Search engine (Elasticsearch 6)

AIaaS(APIs)

- Autocomplete- Did you mean…?

- Suggest as you type- More like this- Percolators- Similarity

- Image classification- Speech recognition

- Face detection

Data Drivenapplication infrastructure

(WeDeploy)

- Search classification- Customer interest prediction

- Machine learning trained models- Recommendation engines


Thank youand many discoveries at #LRDEVCON

Intelligent Information Discovery: Machine Driven Search - Liferay DEVCON 2017, Amsterdam,...

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