A presentation done by studying the history research in the field of Mind Mapping as a Part of M tech Part I final seminars.

1. M S . S U N A Y A N A G A W D E
M . T E C H . P A R T I
1 4 1 0 9
MIND MAPPING
AND ITS
APPLICATIONS,
INTRODUCTION
TO CONTEXT
TREES

2. MIND MAP CONCEPT DEFINITION
 Mind-mapping is a technique to record and organize
information, and to develop new ideas [Holland et al.
2004]
 Mind-maps are similar to outlines and consist of three
elements, namely nodes, connections, and visual clues.
 To begin mind-mapping, users create a root node that
represents the central concept that the users are
interested in [Davies 2011]. To detail the central concept,
users create child-nodes that are connected to the root
node. To detail the child-nodes, users create child-nodes
for the child-nodes, and so on.

3. EXAMPLE

4. MIND MAPS IN HUMAN COMPUTER
INTERACTION
 Faste and Lin [2012] evaluated the effectiveness of
mind- mapping tools and developed a framework
for collaboration based on mind-maps.

5. Document engineering & text mining
 Kudelic et al. [2012] created mind-maps from texts
automatically.
AND
 Bia et al. [2010] utilized mind-maps to model
semi-structured documents, i.e. XML files and the
corresponding DTDs, schemas, and XML
instances.

6. In the field of education
 Jamieson [2012] researched how graph analysis
techniques could be used with mind-maps to
quantify the learning of students.
AND
 Somers et al. [2014] used mind-maps to research
how knowledgeable business school students are.

7. UTILIZING MIND-MAPS IN IR & USER
MODELLING
 By Joeran Beel, Stefan Langer, Marcel Genzmehr,
Bela Gipp
 Published in UMAP 2014
 Presented 8 ideas on how mind mapping can be
used in IR applications
 User modelling was the most feasible use case
 Proposed to implement a prototype- Research
paper recommender system

8. ARCHITECTURE OF DOCEAR’S
RECOMMENDATION SYSTEM
 By Joeran Beel, Stefean Langer, Bela Gipp,
Andreas
 Published in D-lib magazine of Digital Libraries
2014 AND ACM/IEEE Joint Conference on Digital
Libraries 2014
 Introduced 4 datasets which contains metadata
about research articles, details of Docear’s users
and their mind-maps and recommendations they
received.

9. COMPARABILITY OF RECOMMENDER
SYSTEM EVALUATIONS AND
CHARACTERISTICS OF DOCEAR’S USERS
 By Stefan Langer and Joeran Beel
 Published in a workshop: Dimensions and Design at
the ACM RecSys 2014 Conference
 Proved that user characteristics affect the
performance of recommender system.

10. Mind-Map Based User Modelling and Research
Paper Recommendations
 By Joeran Beel, Stefean Langer, Bela Gipp and
Georgia
 Published and Presented in UMAP conference 2015
 User Models were developed based on unique data
from Mind Maps and Recommender system was
integrated with Docear.
 Raised CTR to 9.82%

11. Problem Definition
 To develop a mini-recommender system
 Input from mind maps created by FreeMind
 Giving Recommendations from Google based on the content of
Mind Maps nodes alone.
 Testing

12. Introducing Context Tree Recommender System
 A context-tree recommender system builds a hierarchy of
contexts, arranged in a tree
 Context can be the list of stories read by a user.
 Child node completely contains the context of its parents.
 The root node corresponds to the most general context,
i.e. when no information is available to profile the user
 Recommendations on most popular or most recent stories.
 More the user browses the stories, the more contexts we
are able to extract.
 Deeper Context Trees and finer Recommendations.

13. Example of Context tree

14. Offline and Online Evaluation of News
Recommender Systems at swissinfo.ch
 By Florent Garcin, Olivier D, Christophe Bruttin.
 Published in ACM RecSys 2014, USA.
 CT Recommender System.
 Profiles the users in real time without Log in.
 Improves the CTR by 35%

15. Online CTR with Context Tree

16. Future Work
 CT Recommender System for Audios or Videos
 CTR of Recommender Systems:
 Standard Method: Up to 3.09%
 Mind Map Based: Up to 9.82%
 Context Tree Based: Improved By 35%
What’s Next??

17. REFERENCES
 BEEL, J., LANGER, S., GENZMEHR, M. AND GIP, B., 2014. Utilizing
Mind-Maps for Information Retrieval and User Modelling. Proceedings of
the 22nd Conference on User Modelling, Adaption, and Personalization
(UMAP
 BEEL, J., LANGER, S. AND GIPP, B., 2014. The Architecture and Datasets
of Docear’s Research Paper Recommender System. In Proceedings of the
3rd International Workshop on Mining Scientific Publications (WOSP
2014) at the ACM/IEEE Joint Conference on Digital Libraries (JCDL
2014).
 STEFAN LANGER, BEEL, 2014. Comparability of Recommender System
evaluations and characteristics of docear’s users. In ACM RecSys 2014
conference
 STEFAN LANGER, BEEL, GIP 2014. Mind-Map Based User Modeling and
Research Paper Recommender Systems in ACM Transactions
 www.docear.org
 Florent Garcin, Olivier D, Christophe Bruttin, 2014. Offline and Online
Evaluation of News Recommender Systems at swissinfo.ch in ACM RecSys
2014.

18. THANK YOU