Hierarchy Visualization for Group Recommender Systems

Most recommender systems (RSs), especially group RSs, focus on methods and accuracy but lack explanations, hence users find them difficult to trust. We present a hierarchy visualization method for group recommender (HVGR) systems to provide visual presentation and intuitive explanation. We first use a hierarchy graph to organize all the entities using nodes (e.g., neighbor nodes and recommendation nodes) and illustrate the overall recommender process using edges. Second, a pie chart is attached to every entity node in which each slice represents a single member, which makes it easy to track the influence of each member on a specific entity. HVGR can be extended to adapt different pseudouser modeling methods by resizing group member nodes and pseudouser nodes. It can also be easily extended to individual RSs through the use of a single member group. An implementation has been developed and feasibility is tested using a real data set.

[1]  Barry Smyth,et al.  PeerChooser: visual interactive recommendation , 2008, CHI.

[2]  Helen C. Purchase,et al.  Which Aesthetic has the Greatest Effect on Human Understanding? , 1997, GD.

[3]  Tobias Höllerer,et al.  SmallWorlds: Visualizing Social Recommendations , 2010, Comput. Graph. Forum.

[4]  Jie Lu,et al.  A fuzzy content matching-based e-Commerce recommendation approach , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[5]  Wei Wang,et al.  Collaborative Filtering with Entropy‐Driven User Similarity in Recommender Systems , 2015, Int. J. Intell. Syst..

[6]  Stuart E. Middleton,et al.  Ontological user profiling in recommender systems , 2004, TOIS.

[7]  Wei Wang,et al.  Recommender system application developments: A survey , 2015, Decis. Support Syst..

[8]  Alexandros Nanopoulos,et al.  Item Recommendation in Collaborative Tagging Systems , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Peter Eades,et al.  A Heuristic for Graph Drawing , 1984 .

[10]  Yifan Hu,et al.  Putting recommendations on the map: visualizing clusters and relations , 2009, RecSys '09.

[11]  Bart P. Knijnenburg,et al.  Explaining the user experience of recommender systems , 2012, User Modeling and User-Adapted Interaction.

[12]  Kim Marriott,et al.  Compact Layout of Layered Trees , 2007, ACSC.

[13]  Jie Lu,et al.  A trust-semantic fusion-based recommendation approach for e-business applications , 2012, Decis. Support Syst..

[14]  Judith Masthoff,et al.  A Survey of Explanations in Recommender Systems , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[15]  Katarzyna Musial,et al.  Multidimensional Social Network in the Social Recommender System , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Michael Kaufmann,et al.  Drawing graphs: methods and models , 2001 .

[17]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[18]  Emilia Gómez,et al.  Semantic audio content-based music recommendation and visualization based on user preference examples , 2013, Inf. Process. Manag..

[19]  Mao Lin Huang,et al.  Space-Optimized Tree: A Connection+Enclosure Approach for the Visualization of Large Hierarchies , 2003, Inf. Vis..

[20]  Guangquan Zhang,et al.  Emergency management evaluation by a fuzzy multi-criteria group decision support system , 2009 .

[21]  Fernando Ortega,et al.  Trees for explaining recommendations made through collaborative filtering , 2013, Inf. Sci..

[22]  Fernando Ortega,et al.  Hierarchical graph maps for visualization of collaborative recommender systems , 2014, J. Inf. Sci..

[23]  Jie Lu,et al.  Web-based Multi-Criteria Group Decision Support System with Linguistic Term Processing Function , 2005, IEEE Intell. Informatics Bull..

[24]  Charalampos Konstantopoulos,et al.  Mobile recommender systems in tourism , 2014, J. Netw. Comput. Appl..

[25]  Xianyi Zeng,et al.  A linguistic multi-criteria group decision support system for fabric hand evaluation , 2009, Fuzzy Optim. Decis. Mak..

[26]  David Maxwell Chickering,et al.  Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..

[27]  David Harel,et al.  Drawing graphs nicely using simulated annealing , 1996, TOGS.

[28]  Jie Lu,et al.  A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System , 2014, IEEE Transactions on Fuzzy Systems.

[29]  Antonio Moreno,et al.  SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities , 2013, Eng. Appl. Artif. Intell..

[30]  Luis Martínez-López,et al.  A Consensus‐Driven Group Recommender System , 2015, Int. J. Intell. Syst..

[31]  Kozo Sugiyama,et al.  Layout Adjustment and the Mental Map , 1995, J. Vis. Lang. Comput..

[32]  Erik Duval,et al.  Visualizing recommendations to support exploration, transparency and controllability , 2013, IUI '13.

[33]  Zhen Wen,et al.  Behavior-driven visualization recommendation , 2009, IUI.

[34]  Edward M. Reingold,et al.  Tidier Drawings of Trees , 1981, IEEE Transactions on Software Engineering.

[35]  Tobias Höllerer,et al.  TasteWeights: a visual interactive hybrid recommender system , 2012, RecSys.