This document discusses utilizing linked data structures for social-aware search applications. It proposes using a scoring model that considers social signals like friendship, social distance, expertise, and interests for ranking search results. Linked data allows a uniform representation of social attributes from different data sources. The approach introduces a social-aware scoring model that can rank search results for e-commerce based on social data. However, privacy regulations may limit access to social data.

1. Chemnitz University of Technology Ÿ Prof. Dr.-Ing. Martin Gaedke & Team 17.10.17
Utilizing Linked Data Structures for
Social-aware Search applications
André Langer, Michael Krug, Luis Moreno and Martin Gaedke
LEDSPLaY @ INFORMATIK2017
VSR://Research/LEDS LEDS
INKED NTERPRISE ATA ERVICESL E D S

2. What we do
2

3. VSR://Research/LEDS/Background 3
Linked Open Data Corporate Data Social Networks
Data Lake
Knowledge Graphs
Management of
Background
Knowledge
Data Quality and
Coherence
Knowledge
Extraction
Search in Linked Data
E-Commerce Applications
Focus of
Interest

4. Initial
Question
4

5. 5VSR://Research/LEDS/Motivation
https://de.statista.com/infografik/642/top-10-online-shops-in-deutschland-nach-umsatz/
https://wearesocial.com/special-reports/digital-in-2017-global-overview

6. Social Commerce (2005-2015)
[…] a subset of electronic commerce that
involves social media, online media that
supports social interaction, and user
contributions to assist online buying and
selling of products and services […]
6VSR://Research/LEDS/Motivation

7. VSR://Research/LEDS/Motivation 7

8. VSR://Research/LEDS/Motivation 8
Good friends

9. 1. How can Social Commerce
applications benefit from
Linked Data?
9VSR://Research/LEDS/Motivation

10. 2. How can a Scoring Model
look like that considers social
signals for ranking purposes?
10VSR://Research/LEDS/Motivation

11. 3. What are current obstacles
for successful personalization
strategies based on social data?
11VSR://Research/LEDS/Motivation

12. Approach
12

13. VSR://Research/LEDS/Approach 13
Assets StoreE-Commerce Shop
Social
Network 1
Social
Network 2
SAS Store
Social-aware Search

14. What are meaningful ranking parameters?
14VSR://Research/LEDS/Approach

15. • Type of friendship
• Social distance
• Expertise
• Personal interests
• Gender
• Age
• Geographic location
• …
15VSR://Research/LEDS/Approach
What are meaningful ranking parameters?

16. VSR://Research/LEDS/Approach 16
Formalized representation

17. VSR://Research/LEDS/Approach 17
Formalized representation

18. VSR://Research/LEDS/Approach 18
Linked Data representation

19. 19VSR://Research/LEDS/Approach

20. Conclusion
20

21. The use of Linked Data allows a
uniform representation and
processing of social attributes and is
capable of combining social data
from heterogeneous data sources
21VSR://Research/LEDS/Conclusion

22. We introduced a Social-aware
Scoring model that can be used for
general adaptive search result
ranking purposes in existing E-
Commerce environments
22VSR://Research/LEDS/Conclusion

23. The trend to restricted access to
social data as well as upcoming data
privacy regulations (EU GDPR) limit
possibilities
23VSR://Research/LEDS/Conclusion

24. VSR
Chemnitz University of Technology Ÿ Prof. Dr.-Ing. Martin Gaedke & Team 17.10.17
Inspired and Interested?
Michael.Krug@Informatik.TU-Chemnitz.de
VSR.Informatik.TU-Chemnitz.de
@myVSR /myVSR
LEDS
INKED NTERPRISE ATA ERVICESL E D S