Iletken recommendation technologies solution

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Social Recommendation Technologies

description

iletken recommendation technologies iletken tavsiye sistemleri tanıtım

Transcript of Iletken recommendation technologies solution

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Social Recommendation Technologies

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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?

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…Lost Business Opportunity

User Frustration…

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Increase Usage and Sales between %10-50

by

connecting

the right content the right user

with

* iletken for Mobile Content Recommendations slide

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Understand the User

For Giving

Understand the Content

Right Content

to the

Right User

You Need To

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ContentSocial &

User Network

User action

Business Client

Interactions Content and Context

Real Time Recommendati

ons

Analytics and

Feedback

Customized Solution

iletken Recommender System

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BenefitsBenefits

Monetize Niche Content

Generate Cross Sales

Increase Usability

The bottom line is…

Targeted Reach

… and more

Sales Increase10% - 50%

Better Customer Service

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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

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Our Hybrid TechnologyOur Hybrid Technology

vs

Behavior based Content basedSocial Relevancy basedContext based proximity graphs

Collaborative filtering

Natural language processing

Metadata analysis

Machine Learning

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About iletken TechnologiesAbout iletken Technologies

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iletken for Media Content Recommendationsiletken for Media Content Recommendations

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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

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iletken for E-Commerce Recommendationsiletken for E-Commerce Recommendations

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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

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ThanksContact [email protected] http://www.iletken-project.com/

Next: More on recommender technologyNext: More on recommender technology

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Next: More on Recommendation TechnologiesNext: More on Recommendation Technologies

1. Real World Example: Salesman

2. Recommendation Methods Detailed

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The Salesman Analogy

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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

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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

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iletken’s award winning social approach

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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

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Wisdom of the Crowds Circle of Trust

İletken’s Trust Networksİletken’s Trust Networks

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Let’s ask Keith about music

Semi-Exclusive Trust NetworksSemi-Exclusive Trust Networks

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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 !

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Rock and Roll

Politics

Soccer

Different trust networks for different areas of interest

Semi-Exclusive Trust NetworksSemi-Exclusive Trust Networks

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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

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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

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ThanksContact [email protected] http://www.iletken-project.com/

Next: Time to contact iletkenNext: Time to contact iletken