Iletken recommendation technologies solution

  • Published on
    15-Jan-2015

  • View
    2.473

  • Download
    2

Embed Size (px)

DESCRIPTION

iletken recommendation technologies iletken tavsiye sistemleri tantm

Transcript

  • 1. Social Recommendation Technologies

2. 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
3. User Frustration
Lost Business Opportunity
4. with
Increase Usage and Sales between%10-50
by
connecting
the right content
the right user
* iletken for Mobile Content Recommendations slide
5. You Need To
Understand the User
Understand the Content
For Giving
Right Content
to the
Right User
6. Content
Social & User Network
User action
iletken Recommender System
Interactions
Content and Context
Customized Solution
Business
Client
Analytics and Feedback
Real Time Recommendations
7. Benefits
Monetize Niche Content
The bottom line is
Generate Cross Sales
Increase Usability
Sales Increase
10% - 50%
Better Customer Service
Targeted Reach
and more
8. Awards and Global Recognition
3rd best recommender startup at ACMs RecSys08
out of 26 projects from 15 countries worldwide
GeleceninternetindeTrkimzas.
CNN Trk 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 TBTAK
9. Our Hybrid Technology
Behavior based
Content based
Social Relevancybased
Context based proximity graphs
Natural language processing
Collaborative filtering
Metadata analysis
MachineLearning
vs
10. About iletken Technologies
11. iletken for Media Content Recommendations
12. 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
13. iletken for E-Commerce Recommendations
14. Management Team
Seluk ATLI - CIO

  • Semantic Web and Recommender Systems LAB, TW

15. Fulbright Scholar and M.S. Information Technology @ RPIM. Deniz OKTAR - CEO

  • Founded ReklamGiy

Bar Can DAYLIK - CTO

  • Natural Language Processing & MachineLearning

16. Pardus commiter