Iletken recommendation technologies solution

Social Recommendation Technologies

It is the browsing that holds the golden opportunity for a recommendation system, because the user is not focused on finding a specific thing – she is open to suggestions. Alex Iskold, ReadWriteWeb 2007

Recommending items of interest to users

based on explicit or implicit preferneces

Problem?

…Lost Business Opportunity

User Frustration…

Increase Usage and Sales between %10-50

by

connecting

the right content the right user

with

* iletken for Mobile Content Recommendations slide

Understand the User

For Giving

Understand the Content

Right Content

to the

Right User

You Need To

ContentSocial &

User Network

User action

Business Client

Interactions Content and Context

Real Time Recommendati

ons

Analytics and

Feedback

Customized Solution

iletken Recommender System

BenefitsBenefits

Monetize Niche Content

Generate Cross Sales

Increase Usability

The bottom line is…

Targeted Reach

… and more

Sales Increase10% - 50%

Better Customer Service

Awards and Global RecognitionAwards and Global Recognition

3rd best recommender startup at ACM’s RecSys’08…

… out of 26 projects from 15 countries worldwide

“Geleceğın internetinde Türk imzası.” CNN Türk ’08

“One of 5 early recommendation technologies that could shake up their niches.”ReadWriteWeb ‘08

iletken is a proud software partner of intel

iletken R&D is supported by TÜBİTAK

Our Hybrid TechnologyOur Hybrid Technology

vs

Behavior based Content basedSocial Relevancy basedContext based proximity graphs

Collaborative filtering

Natural language processing

Metadata analysis

Machine Learning

About iletken TechnologiesAbout iletken Technologies

iletken for Media Content Recommendationsiletken for Media Content Recommendations

iletken for Mobile Content Recommendationsiletken for Mobile Content Recommendations

Personalized targeting for…

%331 Elevation on Niche Content

%411 Elevation on Popular Content

Overall %35-50 increase in subscription

… mobile game downloads and melodiesLife – Ukraine results

iletken for E-Commerce Recommendationsiletken for E-Commerce Recommendations

Management TeamManagement Team

Selçuk ATLI - CIO Selçuk ATLI - CIO

M. Deniz OKTAR - CEOM. Deniz OKTAR - CEO

Barış Can DAYLIK - CTOBarış Can DAYLIK - CTO

• Semantic Web and Recommender Systems LAB , TW• Fulbright Scholar and M.S. Information Technology @ RPI

• Founded ReklamGiy

• Natural Language Processing & Machine Learning• Pardus commiter

ThanksContact info@iletken-project.comVisit http://www.iletken-project.com/

Next: More on recommender technologyNext: More on recommender technology

Next: More on Recommendation TechnologiesNext: More on Recommendation Technologies

1. Real World Example: Salesman

2. Recommendation Methods Detailed

The Salesman Analogy

Recommending the right house for the right family

Difficult but why? • Needs to know about the item

• Needs to know about the buyers

• Needs experience

A salesmen is a Recommender

Understand the Content - Content based filtering• My knowledge: I have a 3 room, luxury house

Understand Users - Collaborative filtering• My Experience: If the customer lived in NYC, she will live in

NYC• My Experience: One that bought a car is likely to buy a house• My Experience: Customers that are not married rents

iletken’s award winning social approach

İletken’s Recommendation Technology Solutions Detailedİletken’s Recommendation Technology Solutions Detailed

Over 15 Recommendation Algorithms

Developed , Tweaked & Combined• Content Based• Collaborative Based• Social Based

For each spesific business

• Mobile Operator Recommendations• Music/Video Recommendations• E-Commerce Recommendations

Wisdom of the Crowds Circle of Trust

İletken’s Trust Networksİletken’s Trust Networks

Let’s ask Keith about music

Semi-Exclusive Trust NetworksSemi-Exclusive Trust Networks

Let’s ask Keith about politics

Semi-Exclusive Trust NetworksSemi-Exclusive Trust Networks

Trust each user for a spesific field

He might be your expert on music but definetly not politics !

Rock and Roll

Politics

Soccer

Different trust networks for different areas of interest

Semi-Exclusive Trust NetworksSemi-Exclusive Trust Networks

Two Collaborative Filtering Systems ExampleTwo Collaborative Filtering Systems Example

1. Neighboring based methods

2. Matrix Factorization methods

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Melody Services Proximity

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iletken’s Semi-Exclusive Neighbor Algorithmiletken’s Semi-Exclusive Neighbor Algorithm

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Java Games Proximity

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iletken’s Semi-Exclusive Neighbor Algorithmiletken’s Semi-Exclusive Neighbor Algorithm

Factor 1

Factor 2

Factor 3

Factor 4

Factor 1

Factor 2

Factor 3

Factor 4

iletken’s Matrix Factorization Methodsiletken’s Matrix Factorization Methods

Data driven relevancy factors

ThanksContact info@iletken-project.comVisit http://www.iletken-project.com/

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