iletken recommendation technologies solution
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iletken recommendation technologies iletken tavsiye sistemleri tanıtımTRANSCRIPT
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 [email protected] 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 [email protected] http://www.iletken-project.com/
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