This document proposes recommendation systems and services from DataRNK.com. It defines recommender systems as estimating user preferences to provide relevant information and describes how recommendations have significantly contributed to sales for Netflix, LinkedIn, and Amazon. It then outlines DataRNK's recommendation solutions for e-commerce, jobs, and custom use cases, how they offer a platform and API for scalable recommendations, and how the impact of recommendations can be measured.

1. Morefreepresentations:www.slideseller.com
Personalized
Recommendations
@ DataRNK.com

2. RECOMMENDATIONS
WORTH IN
BILLIONS
What’s common for all these brands ?
They provide us with
personalized recommendations !
PROPOSAL

3. PROPOSAL
WHAT ARE
RECOMMENDER SYSTEMS?
Recommender systems support users in finding
relevant information in an overloaded information space
Estimate a utility function that automatically predicts how much a user (e.g., customer)
will like an item (e.g., product, job, article, etc.)
Based on:
– Past behavior
– Relations to other users
– Item similarity
– Context
– …

4. 75%
PROPOSAL
WHY USE
RECOMMENDATIONS ?
Netflix
rented movies are from
recommendations
50%
LinkedIn
job applications due to
recommendations
35%
Amazon
are sales from
recommendations

5. PROPOSAL
EVERYTHING IS A
RECOMMENDATION
The complete content a user sees on
Netflix is actually a recommendation

6. PROPOSAL
EVERYTHING IS A
RECOMMENDATION
XING and LinkedIn display relevant jobs even
before the user explicitly searches for them
Amazon boosts its sales by always showing
relevant items to the current purchase

7. DataRNK
SOLUTIONS
PROPOSAL

8. ShopRNK
Supports e-commerce recommendation use-cases:
– Trending
• Recommend “hot” products that are currently trending in your customer base
– Alternatives
• Recommend similar alternative products to the one the customer is currently looking at
– Index
• Recommend already at the home page products that are relevant for the customer
– Cross-Selling
• Recommend additional products that are relevant for the current purchase
PROPOSAL

9. JobRNK
Supports recruiting recommendation use-cases:
– People
• Recommend people you may want to professionally connect with
– Related Jobs
• Recommend related alternative jobs to the one the customer is currently looking at
– InMail Jobs
• Recommend relevant job opportunities to enrich personalized e-mails for potential job-seekers
– Companies
• Recommend relevant companies that could interest the job-seeker
PROPOSAL

10. CustomRNK
PROPOSAL
But, what works for my special use-case ?
– It depends on the domain and the particular problem
Setup of custom recommendation services:
– Specifically tailored for the particular business requirements
– Help from our specialists to achieve the best conversion rate

11. WHAT WE OFFER
PROPOSAL
– A platform which serves AI powered recommendations
to boost customer satisfaction and increase revenue
– Scalable solution to deliver the recommendations no matter
how large the traffic is
– Easy integration with any system using our RESTful API
– Measurable impact of the employed recommendation algorithms

12. PROPOSAL
EASY INTEGRATION
Interactions
Interactions
Recommendations
Data Catalog
Recommendations
User
Frontend
Server
Backend
DataRNK
Cloud API

13. PROPOSAL
DataRNK Dashboard
Track and measure how much improvement
was gained by using recommendations
MEASURE THE
IMPACT

14. YOU WANT TO KNOW MORE ?
We would love to discuss what recommendation solution
would help you with your project !
info@datarnk.com
PROPOSAL