Data Mining as an Engine of Personalization

Post on 28-Jan-2015

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description

People are no longer satisfied with flat, single-output websites that do not personalize to the needs and differences of each viewer. With the wealth of data and interaction mining techniques being employed in everything from online sites to brick and mortar stores, we are truly seeing a major industry shift towards automatic personalization. This session will cover the concepts of long-term personalization and on-demand emotional state interaction, which in turn can be used as the architecture to drive commerce and personalization.

Transcript of Data Mining as an Engine of Personalization

ENTERPRISE IT 20 x 20

Data Mining as an Engine of Personalization

Jonathan LeBlanc (@jcleblanc)

The Web is Becoming Personal

Premise

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

Then I Read This…

Us & Them

The Science of Identity

By David Berreby

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

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

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

Pay special attention to high value tags & content location

Expanding to Phrases

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

Seems easy right? Just like single words

Working with Unknown Users

The majority of users won’t be immediately targetable

Tracking Emotional Change

You have to be aware of personality changes

Tracking users as they use your service

Using On Demand Tracking

Traits of the BoredDistractionRepetitionTiredness

Reasons for BoredomLack of interestReadiness

Adding in Time Interactions

Time and interaction need to be accounted for

Gift buying seasons see interest variations

Grouping Using Commonality

InterestsUser A

InterestsUser B

Inte

rests

Com

mon

A Closing Thought

Just because you can do something, doesn’t mean you

should