Text of Mind mapping and Its Applications, Introduction to Context Trees
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.
4. MIND MAPS IN HUMAN COMPUTER INTERACTION Faste and Lin  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.  created mind-maps from texts automatically. AND Bia et al.  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  researched how graph analysis techniques could be used with mind-maps to quantify the learning of students. AND Somers et al.  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 DOCEARS 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 Docears users and their mind-maps and recommendations they received.
9. COMPARABILITY OF RECOMMENDER SYSTEM EVALUATIONS AND CHARACTERISTICS OF DOCEARS 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 prole 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. Ofine 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% Whats 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 Docears 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 docears 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. Ofine and Online Evaluation of News Recommender Systems at swissinfo.ch in ACM RecSys 2014.