Recommender systems aim at helping users to find relevant information in an overloaded information space. Although there are well known methods (Content-based, Collaborative Filtering, Matrix Factorization) and libraries to implement, evaluate and extend recommenders (Apache Mahout, Graphlab, MyMediaLite, among others), the deployment of a real-time recommender from scratch which considers a combination of algorithms and various data sources (e.g., social, transactional, and location) remains unsolved. In this talk, we report on the challenges towards such a recommender systems in the context of online of offline marketplaces. In particular, we describe our solution in terms of the requirements, the data model and algorithms that allows modularity and extensibility, as well as the system architecture to facilitate the scaling of our approach to big data for online and offline marketplaces.

1. Towards a Big Data Recommender
Engine
For Online and Offline Marketplaces
Martin Kahr (Blanc-Noir)
Christoph Trattner (Know-Center)

2. INHALT
Background and Introduction
About Blanc-Noir│ Our Vision │ What we do
Partnership with Know-Center
Partnership │ Challenge and Goal│ Output
Recommender Engine
xxxx │ xxxx
Q&A

3. ABOUT BLANC-NOIR
Headquarter: Graz (Austria)
Subsidiaries: Ingolstadt (Germany)
Vienna, Klagenfurt,
Founded: 2012
Experience: More than 18 years in IT & Marketing
Employees: 60

4. ABOUT BLANC-NOIR
Blanc-Noir combines Know-How in
marketing and technology
to create innovative and trendsetting
solutions for online and stationary
trade.

5. OUR VISION
We want to change the buying
behavior of customers and to realize
a unique and sustainable shopping
experience.

6. 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

7. WHAT WE DO (EXAMPLES)
Digital Loyality Card
On- and offline collection and redeem
of bonus points
Mobil
Payment
NFC, Beacon
(Bluetooth 4.0)
Endless-aisle
Mobile catalogue and
Mobil shopping

8. 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

9. 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.

10. OUR CHALLENGE AND GOAL
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
Knowing what the customer
thinks, and desires!

11. OUTPUT
Cross-Channel
customer understanding
and realtime targeting

12. How did we manage to handle this
challenge?

13. RECOMMENDER SYSTEMS

14. 14

15. 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

16. THE FRAMEWORK

17. HOW does the thing perform?
Dataset of virtual world SecondLife: Marketplace and social data
17

18. FOLLOW-UP (2)

19. RECSIUM FRAMEWORK

20. ...CURRENTLY WORKING ON
• Location-based services shopping malls, train-stations
• Technology: iBeacons
• Task: indoor navigation, indoor marketing, etc...

21. ...CURRENTLY WORKING ON

22. DEMO - RECSIUM
http://recsium.know-center.tugraz.at/recsium/

23. THANK YOU
ANY QUESTIONS?