building an identity extraction engine
DESCRIPTION
When it comes to building customized experiences for your users, the biggest key is in understanding who those users are and what they're interested in. The largest problem with the traditional method for doing this, which is through a profile system, is that this is all user-curated content, meaning that the user has the ability to enter in whatever they want and be whoever they want. While this gives people the opportunity to portray themselves how they wish to the outside world, it is an unreliable identity source because it's based on perceived identity. In this session we will take a practical look into constructing an identity entity extraction engine, using PHP, from web sources. This will deliver us a highly personalized, automated identity mechanism to be able to drive customized experiences to users based on their derived personalities. We will explore concepts such as: - Building a categorization profile of interests for users using web sources that the user interacts with. - Using weighting mechanisms, like the Open Graph Protocol, to drive higher levels of entity relevance. - Creating personality overlays between multiple users to surface new content sources. - Dealing with users who are unknown to you by combining identity data capturing with HTML5 storage mechanisms.TRANSCRIPT
Identit
y Data
Min
ing
Building an Id
entity D
ata M
ining Engine in
PHPJonath
an LeBlanc (
@jcl
eblanc)
Premise
You can determine the personality profile of a person based on their browsing habits
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 unstructured
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(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
Thank You!
Questio
ns?
www.slidesh
are.co
m/jc
leblanc