Group recommendation system: Focusing on home group user in TV domain

Group recommendation system is an important research area, because there are many situations where a group user take an item such as watching the movie, listening the music and watching the TV contents with their family or friends. In order to suggest group recommendation, understanding of group user and domain is necessary. However, there are few analysis of group recommendation system using real-world dataset, because most researches use synthetic dataset. In this paper, we apply the various methods to real-world dataset, and provide some guides for group recommendation system.

[1]  John Riedl,et al.  PolyLens: A recommender system for groups of user , 2001, ECSCW.

[2]  Sebastiano Pizzutilo,et al.  Group modeling in a public space: methods, techniques, experiences , 2005 .

[3]  Jesús Bobadilla,et al.  A new collaborative filtering metric that improves the behavior of recommender systems , 2010, Knowl. Based Syst..

[4]  Jee-Hyong Lee,et al.  A music recommendation system with a dynamic k-means clustering algorithm , 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

[5]  Jagadeesh Gorla,et al.  Probabilistic group recommendation via information matching , 2013, WWW.

[6]  Jee-Hyong Lee,et al.  Personalized Expert-Based Recommender System: Training C-SVM for Personalized Expert Identification , 2013, MLDM.

[7]  José Juan Pazos-Arias,et al.  TV program recommendation for groups based on muldimensional TV-anytime classifications , 2009, IEEE Transactions on Consumer Electronics.

[8]  Keon-Myung Lee Adaptive Resource Scheduling for Workflows Considering Competence and Preference , 2004, KES.

[9]  Eun Yi Kim,et al.  Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating , 2009, IEEE Transactions on Consumer Electronics.

[10]  Derek G. Bridge,et al.  A Case-Based Solution to the Cold-Start Problem in Group Recommenders , 2012, ICCBR.

[11]  Jee-Hyong Lee,et al.  Implementation of Ontology Based Context-Awareness Framework for Ubiquitous Environment , 2007, 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07).

[12]  Francesco Ricci,et al.  Group recommendations with rank aggregation and collaborative filtering , 2010, RecSys '10.

[13]  Joseph F. McCarthy,et al.  MUSICFX: an arbiter of group preferences for computer supported collaborative workouts , 2000, CSCW '00.

[14]  Ludovico Boratto,et al.  Modeling the Preferences of a Group of Users Detected by Clustering: a Group Recommendation Case-Study , 2014, WIMS '14.