MIND MAP BASED USER MODELLING AND RECOMMENDER SYSTEMS

Ms. Sunayana GawdeM.Tech. Part I14109

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.

EXAMPLE

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.

In the field of document engineering and 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.

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.

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

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.

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.

• 3 Approaches of Content Based Filtering to build user models:

• Use the terms from last edited node (CTR 0.2% - 1%) (MindMeister)• All terms in User’s current Mind-map• All terms from all Mind-maps user has ever created.

METHODOLOGY• 28 variables• Number of mind-maps• Number of nodes• Size of user model• Whether to use only visible nodes• Weighting schemes

• Docear’s mind-map specific user modelling approach

RESULTS:

• Mind-map and node selection• Mind-map selection• Node selection• Node extension

• Node and feature weighting• User model size

MIND-MAP SELECTION

NODE SELECTION

BASED ON NUMBER OF DAYS

BASED ON NODE MODIFICATION TYPE

BASED ON NODE VISIBILITY

NODE EXTENSION

NODE AND FEATURE WEIGHTING• Based on depth of a node

(weighted stronger the deeper they are- improved CTR of 5.62% from 5.15%)• Based on number of children (weighted stronger

for more number of children- improved CTR of 5.17% from 5.01%)

• Based on number of siblings (More siblings-Higher weights – improved CTR of 5.41% from 5.01%)

USER MODEL SIZE

Docear’s Mind-map Specific UM Approach:• Combined optimal values of all variables in single

algorithm• Used 75 Most Recently MOVED nodes from past

90 days.• Nodes Expansion• Term Weighting (TF-IDuF)• 35 highest weighted nodes were User Models• Comparison with 4 baselines• Stereotype• Recently modified node• All nodes of current mind-map• All nodes of all mind-maps

Results:

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

THANK YOU