User Similarity and Deviation Analysis for Adaptive Visualizations

Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aims to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations.

[1]  Lin Guo,et al.  A Combinative Similarity Computing Measure for Collaborative Filtering , 2013 .

[2]  Arjan Kuijper,et al.  A Reference Model for Adaptive Visualization Systems , 2011, HCI.

[3]  Songjie Gong.  A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering , 2010, J. Softw..

[4]  Derek H. Sleeman,et al.  UMFE: A User Modelling Front-End Subsystem , 1985, Int. J. Man Mach. Stud..

[5]  Arjan Kuijper,et al.  Modeling Users for Adaptive Semantics Visualizations , 2011, HCI.

[6]  Peter Brusilovsky,et al.  User Models for Adaptive Hypermedia and Adaptive Educational Systems , 2007, The Adaptive Web.

[7]  Constantine Stephanidis,et al.  Universal Access in Human-Computer Interaction , 2011 .

[8]  Kawa Nazemi,et al.  Interaction analysis: An algorithm for interaction prediction and activity recognition in adaptive systems , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[9]  Jimeng Sun,et al.  HiMap: Adaptive visualization of large-scale online social networks , 2009, 2009 IEEE Pacific Visualization Symposium.

[10]  Giuseppe Santucci,et al.  Ontology Visualization: One Size Does Not Fit All , 2012, EuroVA@EuroVis.

[11]  Pat Hanrahan,et al.  Show Me: Automatic Presentation for Visual Analysis , 2007, IEEE Transactions on Visualization and Computer Graphics.

[12]  Jae-wook Ahn,et al.  Adaptive Visualization for Focused Personalized Information Retrieval. , 2010 .

[13]  Peter Brusilovsky,et al.  Adaptive Knowledge-Based Visualization for Accessing Educational Examples , 2006, Tenth International Conference on Information Visualisation (IV'06).

[14]  Cristina Conati,et al.  User Modeling 2007, 11th International Conference, UM 2007, Corfu, Greece, June 25-29, 2007, Proceedings , 2007, User Modeling.

[15]  Kawa Nazemi,et al.  Adaptive Semantic Visualization for Bibliographic Entries , 2013, ISVC.

[16]  Kawa Nazemi,et al.  Interaction Analysis for Adaptive User Interfaces , 2010, ICIC.

[17]  Panagiotis Symeonidis,et al.  Feature-Weighted User Model for Recommender Systems , 2007, User Modeling.

[18]  Andreas Kerren,et al.  Knowledge Generation through Human-Centered Information Visualization , 2009, Inf. Vis..

[19]  Julie A. Jacko.  Design and development approaches , 2011 .

[20]  Akrivi Katifori,et al.  Context and Adaptivity-Driven Visualization Method Selection , 2009 .

[21]  Jie Lu,et al.  HARVEST: an intelligent visual analytic tool for the masses , 2010 .

[22]  Akrivi Katifori,et al.  A Context-Based Adaptive Visualization Environment , 2006, Tenth International Conference on Information Visualisation (IV'06).

[23]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[24]  Arjan Kuijper,et al.  Interacting with Semantics: A User-Centered Visualization Adaptation Based on Semantics Data , 2011, HCI.

[25]  Pedro M. Domingos,et al.  Relational Markov models and their application to adaptive web navigation , 2002, KDD.

[26]  Ruimin Shen,et al.  A collaborative filtering framework based on both local user similarity and global user similarity , 2008, Machine Learning.

[27]  Peter Brusilovsky,et al.  Adaptive Visualization of Search Results: Bringing User Models to Visual Analytics , 2009, Inf. Vis..

[28]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.