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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
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