Using Identity Data Mining

Securing & Personalizing Commerce

Jonathan LeBlancDeveloper Evangelist (PayPal)

Github: http://github.com/jcleblancTwitter: @jcleblanc

The Problem

Commerce Relies on Static Data Contributions

Premise

You can determine the personality profile of a person based on their usage habits

Personalization == Security

Technology was the Solution!

Then I Read This…

Us & Them

The Science of Identity

By David Berreby

The Different States of Knowledge

What a person knows

What a person knows they don’t know

What a person doesn’t know they don’t know

Technology was NOT the Solution

Identity and discovery are

NOT a technology solution

Our Subject Material

Our Subject Material

HTML content is poorly structured

There are some pretty bad web practices on the interwebz

You can’t trust that anything semantically valid will be present

How We’ll Capture This Data

Start with base linguistics

Extend with available extras

The Com

ponents

The Basic Pieces

Page Data

Scrapey Scrapey

Keywords Without all

the fluff

WeightingWord diets

FTW

Capture Raw Page Data

Semantic data on the webis sucktastic

Assume 5 year olds built the sites

Language is the key

Extract Keywords

We now have a big jumble of words. Let’s extract

Why is “and” a top word? Stop words = sad panda

Weight Keywords

All content is not created equal

Meta and headers and semantics oh my!

This is where we leech off the work of others

Simple

Ext

ract

ion E

ngine

Questions to Keep in Mind

Should I use regex to parse web content?

How do users interact with page content?

What key identifiers can be monitored to detect interest?

Fetching the Data: The Request

$html = file_get_contents('URL');

$c = curl_init('URL');

The Simple Way

The Controlled Way

Fetching the Data: cURL$req = curl_init($url);

$options = array( CURLOPT_URL => $url, CURLOPT_HEADER => $header, CURLOPT_RETURNTRANSFER => true, CURLOPT_FOLLOWLOCATION => true, CURLOPT_AUTOREFERER => true, CURLOPT_TIMEOUT => 15, CURLOPT_MAXREDIRS => 10 );

curl_setopt_array($req, $options);

//list of findable / replaceable string characters $find = array('/\r/', '/\n/', '/\s\s+/'); $replace = array(' ', ' ', ' '); //perform page content modification $mod_content = preg_replace('#<script(.*?)>(.*?)</ script>#is', '', $page_content); $mod_content = preg_replace('#<style(.*?)>(.*?)</     style>#is', '', $mod_content);

$mod_content = strip_tags($mod_content);$mod_content = strtolower($mod_content);$mod_content = preg_replace($find, $replace, $mod_content); $mod_content = trim($mod_content);$mod_content = explode(' ', $mod_content);

natcasesort($mod_content);

//set up list of stop words and the final found stopped list$common_words = array('a', ..., 'zero'); $searched_words = array();

//extract list of keywords with number of occurrences foreach($mod_content as $word) { $word = trim($word); if (preg_match('/[^a-zA-Z]/', $word) == 1){ $word = ''; }     if(strlen($word) > 2 && !in_array($word, $common_words)){         $searched_words[$word]++;     } }

arsort($searched_words, SORT_NUMERIC);

Scraping Site Meta Data

//load scraped page data as a valid DOM document $dom = new DOMDocument(); @$dom->loadHTML($page_content);

//scrape title $title = $dom->getElementsByTagName("title"); $title = $title->item(0)->nodeValue;

//loop through all found meta tags $metas = $dom->getElementsByTagName("meta"); for ($i = 0; $i < $metas->length; $i++){ $meta = $metas->item($i);   if($meta->getAttribute("property")){ if ($meta->getAttribute("property") == "og:description"){       $dataReturn["description"] = $meta->getAttribute("content");     }   } else { if($meta->getAttribute("name") == "description"){      $dataReturn["description"] = $meta->getAttribute("content");     } else if($meta->getAttribute("name") == "keywords”){       $dataReturn[”keywords"] = $meta->getAttribute("content");     }   } }

Extendin

g the E

ngine

Weighting Important Data

Tags you should care about: meta (include OG), title, description, h1+, header

Bonus points for adding in content location modifiers

Weighting Important Tags

//our keyword weights$weights = array("keywords" => "3.0",                             "meta" => "2.0",                             "header1" => "1.5",                             "header2" => "1.2");

//add modifier hereif(strlen($word) > 2 && !in_array($word, $common_words)){     $searched_words[$word]++; }

Expanding to Phrases

2-3 adjacent words, making up a direct relevant callout

Seems easy right? Just like single words

Language gets wonky without stop words

Working with Unknown Users

The majority of users won’t be immediately targetable

Use HTML5 LocalStorage & Cookie backup

Adding in Time Interactions

Interaction with a site does not necessarily mean interest in it

Time needs to also include an interaction component

Gift buying seasons see interest variations

Grouping Using Commonality

InterestsUser A

InterestsUser B

Inte

rests

Com

mon

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Thank You! Questions?

Jonathan LeBlancDeveloper Evangelist (PayPal)

Github: http://github.com/jcleblancTwitter: @jcleblanc