towards a big data recommender engine for online and offline marketplaces

Towards a Big Data Recommender

EngineFor Online and Offline Marketplaces

Martin Kahr (Blanc-Noir)

Christoph Trattner (Know-Center)

INHALT

Background and Introduction About Blanc-Noir│ Our Vision │ What we do

Partnership with Know-CenterPartnership │ Challenge and Goal│ Output

Recommender Enginexxxx │ xxxx

Q&A

ABOUT BLANC-NOIR

Headquarter: Graz (Austria)

Subsidiaries: Ingolstadt (Germany)

Vienna, Klagenfurt,

Founded: 2012

Experience: More than 18 years in IT & Marketing

Employees: 60

ABOUT BLANC-NOIR

Blanc-Noir combines Know-How in

marketing and technology

to create innovative and trendsetting

solutions for online and stationary

trade.

OUR VISION

We want to change the buying

behavior of customers and to realize

a unique and sustainable shopping

experience.

WHAT WE DO

• We develop analogue and digital marketing

strategies and campaigns

• Consulting, conception and programming of

E-Commerce and Multi-Channel platforms.

• Development of powerful promotion tools to

increase customer loyalty and shopping

experience.

• Pioneer in the area of Location Based Marketing

and Beacon-Technology

• Cross-Channel Order-Management System

WHAT WE DO (EXAMPLES)

Digital Loyality CardOn- and offline collection and redeem

of bonus points

Mobil

PaymentNFC, Beacon

(Bluetooth 4.0)

Endless-aisleMobile catalogue and

Mobil shopping

WHAT WE DO (EXAMPLES)

App for sellers

• Sales support

• Customer service

• Product information

• Endless-aisle

• Cross- & Upsell

• Coupon via Blue-tooth

to customer´s mobile

PARTNERSHIP 1+1=3

By combining the resources and

competences of Know-Center with our

market-driven input,

we are able to realize a tailored and state-of-

the art solution that provides competitive

advantages for us and our clients.

Unique shopping experience and higher conversions

assumes:

• Understanding and analytics of customer needs,

behaviour and preferences based on historic and live

transactions

• Personalized and real-time communication across all

customer touch points

• No spam - Only relevant and useful information

OUR CHALLENGE AND GOAL

Knowing what the customer

thinks, and desires!

OUTPUT

Cross-Channel

customer understanding

and realtime targeting

How did we manage to handle this

challenge?

RECOMMENDER SYSTEMS

14

WHY SOLR?

• „High-performance, full-featured text search engine library“

… but more precise …

• „High-performance, fully-featured token matching and scoring library“

[Grainger, 2012]

… which provides ….

– full-text searches (content-based)

– powerful queries (e.g., MoreLikeThis or Facets)

– (near) real-time data updates (no pre/re-calculations)

– easy schema updates (social data integration)

• Established open-source software (Apache license) with big

community

15

THE FRAMEWORK

HOW does the thing perform?

Dataset of virtual world SecondLife: Marketplace and social data

17

FOLLOW-UP (2)

RECSIUM FRAMEWORK

...CURRENTLY WORKING ON

• Location-based services shopping malls, train-

stations

• Technology: iBeacons

• Task: indoor navigation, indoor marketing, etc...

...CURRENTLY WORKING ON

DEMO - RECSIUM

http://recsium.know-center.tugraz.at/recsium/

ANY QUESTIONS?

THANK YOU