Iletken recommendation technologies solution

Social Recommendation Technologies

Recommending items of interest to users
based on explicit or implicit preferneces
Problem?
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

User Frustration…
…Lost Business Opportunity

with
Increase Usage and Sales between %10-50
by
connecting
the right content
the right user
* iletken for Mobile Content Recommendations slide

You Need To
Understand the User
Understand the Content
For Giving
Right Content
to the
Right User

Content
Social & User Network
User action
iletken Recommender System
Interactions
Content and Context
Customized Solution
Business
Client
Analytics and Feedback
Real Time Recommendations

Benefits
Monetize Niche Content
The bottom line is…
Generate Cross Sales
Increase Usability
Sales Increase
10% - 50%
Better Customer Service
Targeted Reach
… and more

Awards and Global Recognition
3rd best recommender startup at ACM’s RecSys’08…
… out of 26 projects from 15 countries worldwide
“GeleceğıninternetindeTürkimzası.”
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 Technology
Behavior based
Content based
Social Relevancybased
Context based proximity graphs
Natural language processing
Collaborative filtering
Metadata analysis
MachineLearning
vs

About iletken Technologies

iletken for Media Content Recommendations

iletken for Mobile Content Recommendations
Personalized targeting for…
Life – Ukraine results
… mobile game downloads and melodies
%331 Elevation on Niche Content
%411 Elevation on Popular Content
Overall %35-50 increase in subscription

iletken for E-Commerce Recommendations

Management Team
Selçuk ATLI - CIO
Semantic Web and Recommender Systems LAB, TW
Fulbright Scholar and M.S. Information Technology @ RPI
M. Deniz OKTAR - CEO
Founded ReklamGiy
Barış Can DAYLIK - CTO
Natural Language Processing & MachineLearning
Pardus commiter

Thanks
Contact info@iletken-project.com
Visit http://www.iletken-project.com/
Next: More on recommender technology

Next: More on RecommendationTechnologies
1. Real WorldExample: Salesman
2. RecommendationMethodsDetailed

The Salesman Analogy

A salesmen is a Recommender
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

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:Customersthatare notmarriedrents

iletken’s award winning social approach

İletken’s Recommendation Technology Solutions Detailed
Over 15 Recommendation Algorithms
Content Based
Collaborative Based
Social Based
Developed , Tweaked & Combined
For each spesific business
Mobile Operator Recommendations
Music/Video Recommendations
E-Commerce Recommendations

İletken’s Trust Networks
Wisdom of the Crowds
Circle of Trust

Semi-ExclusiveTrust Networks
Let’s ask Keith about music

Trust each user for a spesific field
Let’s ask Keith about politics
He might be your expert on music but definetly not politics !

Different trust networks for different areas of interest
Rock and Roll
Politics
Soccer

Two Collaborative Filtering SystemsExample
1. Neighboring based methods
2. Matrix Factorization methods

iletken’s Semi-Exclusive Neighbor Algorithm
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MelodyServicesProximity

iletken’s Semi-Exclusive Neighbor Algorithm
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Java Games Proximity

iletken’s Matrix Factorization Methods
Factor 1
Factor 1
Factor 2
Factor 2
Factor 3
Factor 3
Factor 4
Factor 4
Data driven relevancy factors

Thanks
Contact info@iletken-project.com
Visit http://www.iletken-project.com/
Next: Time to contact iletken

Iletken recommendation technologies solution

by iletken recommendation technologies on Nov 23, 2009

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Iletken recommendation technologies solution — Presentation Transcript