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Reseach Article

Family Aware TV Program and Settings Recommender

by Thyagaraju GS, Umakant P Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 4
Year of Publication: 2011
Authors: Thyagaraju GS, Umakant P Kulkarni

Thyagaraju GS, Umakant P Kulkarni . Family Aware TV Program and Settings Recommender. International Journal of Computer Applications. 29, 4 ( September 2011), 1-18. DOI=10.5120/3556-4889

@article{ 10.5120/3556-4889,
author = { Thyagaraju GS, Umakant P Kulkarni },
title = { Family Aware TV Program and Settings Recommender },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 4 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-18 },
numpages = {9},
url = { },
doi = { 10.5120/3556-4889 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:14:52.779286+05:30
%A Thyagaraju GS
%A Umakant P Kulkarni
%T Family Aware TV Program and Settings Recommender
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 4
%P 1-18
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

In this paper we are proposing a design of TV program and settings recommendation engine utilizing contextual parameters like personal, social, temporal, mood and activity. In addition to the contextual parameters the system utilize the explicit or implicit user ratings and watching history to resolve the conflict if any while recommending the services .The System is implemented exploiting AI techniques ( like ontology, fuzzy logic ,Bayesian classifier and Rule Base) , RDBMS and SQL Query Processing . The motivation behind the proposed work is i) to improve the user’s satisfaction level and ii) to improve the social relationship between user and TV. The context aware recommender utilizes social context data as an additional input to the recommendation task alongside information of users and tv programs. We have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system for small families.

  1. Min-Cheol Hwang, Le Thanh Ha, Seung-Kyun Kim, and Sung-Jea Ko, Senior member, IEEE . Department of Electronic Engineering, Korea University, Seoul, Korea, 1-4244-0763-X/07/$20.00 ©2007 IEEE “Real-Time Person Identification System for Intelligent Digital TV”.
  2. Z. Yu and X. Zhou, “TV3P_An Adaptive assistant for Personalized TV,” IEEE Trans. Consum. Electron., Vol. 50, No. 1, pp. 393-399,2004
  3. H. Yoon and W. Woo, ‘‘Design and Implementation of a Universal Appliance Controller Based on Selective Interaction Modes,’’ IEEE Trans. Consum. Electron., Vol. 54, No. 4, pp. 1722-1729, 2008..
  4. Dewan:Sunil “ WIRELESS TRANSLATION DEVICE” 200090215394 A1 August 9th 2009. United states patent application kind code
  5. Adrian C. North, David J. Hargreaves, and Jon J. Hargreaves,“Uses of music in everyday life,” Music Perception, vol. 22, no. 1, pp. 41–77, 2004.
  6. Choonsung Shin and Woontack Woo, Member, IEEE “Socially Aware TV Program Recommender for Multiple Viewers” Downloaded on August 9, 2009 at 01:42 from IEEEXplore..
  7. Doug Williams, Marian F Ursu,, Ian Kegel , “An Emergent Role for TV in Social Communication”.
  8. Dr Daniel Chandler, Matthew Ruckwood, Mediated Communication “Will Interactive Television Change the Relationship Between the Viewer and the Television Set? “ , MC10120 10/01/2005.
  9. Doug Riecken , “Perosonalized views of personalization ,” Comm.ACM ,vol 43,n08,2000.
  10. Muhammad Ashad Kabir ,Jun Han and Alan Colman , “Modeling and Coordianting Social Interactions in Pervasive Environments “,
  11. N.Datia ,J Moura-Pires .M.Cardoso ,H.Pita , “Temporal Patterns o fTV Watching For Portugues Viewers “.
  12. Joe Jeffrey ,”High Fidelity Mathematical Models of Social Systems “, Northen Illinois University.
  13. Ray van Brandenburg , Master Thesis Faculty of Electrical Engineering, Mathematics and Computer Science, Design and Analysis of Communication Systems (DACS),University of Twente” Towards multi-user personalized TV services,introducing combined RFID Digest authentication” ,Dec2007,Master thesis
  14. Paul Resnick ,Neophytos Iacovou ,Mitesh Suchak Peter Bergstorm ,and John Riedli ,”Grouplens : An open architecture for collaborative filtering of netnews , “ in Proc.ACM Conf.Computer Supported Cooperative Work ,Chape Hill ,NC,USA ,1994,pp.175 -186.
  15. Annie Chen ,“Context aware collaborative filtering system : Predicting the user’s preference in the ubiquitous computing environment ,” in Location and Context Awareness,pp.244-253 .Springer ,2005
  16. Manos Papagelis, Dimitris Plexousakis,” Qualitative analysis of user-based and item-based prediction algorithms for recommendation agent “,Engineering Applications of Artificial Intelligence 18 (2005) 781–789 “
  17. Manos Papagelis1, 2, Ioannis Rousidis2, Dimitris Plexousakis1, 2, Elias Theoharopoulos3 , “Incremental Collaborative Filtering for Highly- Scalable Recommendation Algorithms”, Work conducted at ICS-FORTH
  18. SongJie Gong , “A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering “,JOURNAL OF SOFTWARE, VOL. 5, NO. 7, JULY 2010, © 2010 ACADEMY PUBLISHER,doi:10.4304/jsw.5.7.745-752
  19. Kyusik Park, Jongmoo Choi, and Donghee Lee , “A Single-Scaled Hybrid Filtering Method for IPTV Program Recommendation “ , INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING , Issue 4, Volume 4, 2010
  20. J.J. Sandvig and Bamshad Mobasher and Robin Burke , “A Survey of Collaborative Recommendation and the Robustness of Model-Based Algorithms “ , Bulletin of the IEEE Computer Society Technical Committee on Data Engineering , Copyright 2008 IEEE.
  21. Michael Hahsler , “Developing and Testing Top-N Recommendation Algorithms for 0-1 Data using recommender lab “ , February 27, 2011 ,
  22. Amancio Bouza, Gerald Reif, Abraham Bernstein , “Probabilistic Partial User Model Similarity for Collaborative Filtering”.
  23. SongJie Gong , “ A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering”,Journal Of Software ,Vol .5.No 7,July2010.
  24. Manos Papgelis ,Dimitris Plexousakis , “Qualitative analysis of user based and item based prediction algorithms for recommendation agents”,Engineering Applications of Artificial Intelligence 18(2005) 781-789 ,
  25. Artur Lugmayr, Tampere University of Technology, Finland Alexandra Pohl, Berlin-Brandenburg (rbb) Innovationsprojekte, Germany Max Mühlhäuser, Technische Universitat Darmstädt, Germany Jan Kallenbach, Helsinki University of Technology, Finland Konstantinos Chorianopoulos, Bauhaus University of Weimar, Germany “Ambient Media and home Entertainment ” Copyright © 2007, IGI Global
  26. Prof. Dr. Artur Lugmayr , Entertainment and Media Production Management Lab. (EMMi Lab.) Tampere University of Technology (TUT), Tampere Finland,keynote address, Connecting the Real World with the Ubiquitous Overlay in ambient Media “ , Keynote address , SAME 2009 in conjunction with AmI 2009, Salzburg, Austria.
  27. Artur Lugmayr & Thomas Risse & Bjoern Stockleben & Kari Laurila & Juha Kaario , “Semantic ambient media—an introduction “ , Multimed Tools Appl (2009) 44:337–359 DOI 10.1007/s11042-009-0282-z , Published online: 6 May 2009 # Springer Science + Business Media, LLC 2009 .
  28. L.Ardissono, C.Gena ,P.Torasso , “Personalized Recommendation of TV Progams”.
  29. Zhiwen Yu ,Xingshe Zhou , Yanbin HAo ,Jianhua Gu , “ TV program recommendation for multiple viewers based on user profile merging “, 16 April 2006 /Published online :10 June 2006 @ Springer Science Science + Business Media B.V.2006,User Model User Adap Iter (2006) 16 :63 -82.
  30. Choonsung Shin and Woontack Woo ,” Conflict Resolution based on User Preference and Service Profile for Context aware Media Services “.
  31. Choonsung Shin and Woontack Woo , “ Conflict Management for Media Services by exploiting Service Profile and User Preference “,UbiPCMM 2005,pp 48 -57
  32. Choonsung Shin and Woontack Woo , History based Conflict Management for Multiuser and Multi services “,
  33. Choonsung Shin and Woontack Woo, “User – Centric Conflict Management for Media Services Using Personal Companions “, ETRI Journal, Volume 29, Number 3, June 2007.
  34. B.I.J. Siljee, I.E. Bosloper, J.A.G. Nijhuis , University of Groningen, Department of Computing Science PO Box 800, 9700 AV Groningen, The Netherlands {b.i.j.siljee, i.e.bosloper, j.a.g.nijhuis}, ” A Classification Framework for Storage and Retrieval of Context “ .
  35. Huadong Wu , “Sensor Data Fusion for Context-Aware Computing Using Dempster-Shafer Theory “,The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania 15213 December 2003.
  36. Pravin Pawar, Andrew Tokmakoff ,”Ontology based Context Aware Service Discovery for Pervasive environments”. .
  37. Jarno Seppanen, Jyri Huopaniemi , “Interactive and Context Aware Mobile Music Experiences “, Proc of the 11th Int Conference on Digital Audio Effects (DAFx-08),Espoo, Finland ,September 1-4 ,2008.
  38. Anind k.Dey and Gregory D Abowd ,”Towards a better understanding of context and context awareness,” Tech.Report GIT-GVU-99-22, Georgia Institute of Tech ., Atlanta , GA ,USA,1999.
  39. T. Strang and C. Linnhoff-Popien “A Context Modeling survey”, Inthe first International Workshop on Advanced context modeling, Reasoning and management, UbiComp 2004.
  40. Moeiz Miraoui, Chakib Tadj and Chokri ben Amar, “ Context Modeling and Context Aware Service Adaptation for pervasive computing systems”, International Journal of Computer and Information Science and Engineering 2008,pp 143 -152.
  41. B.N., Schilit .A context aware system architecture for mobile distributed computing PhD thesis,1995.
  42. D.Dubois and H.Parade ,”An introduction to fuzzy systems,”,Clin Chim Acta ,vol 270,pp-3-29,1998.
  43. Han-Saem Park ,Ji-Oh Yoo and Sung- Bae Cho “ A Context Aware Music Recommendation System Using Fuzzy Bayesian Networks with Utility Theory “,L.Wang et al.(EDs) :FSKD 2006 ,LNAI 4223 , pp. 970 -979 ,2006 ,@ Springer- Verlag Berlin Heidelberg 2006.
  44. H.pan and L.Liu ,”Fuzzy Bayesian networks : A general formalism for representation , inference and learning with hybrid Bayesian networks ,” Int J Pattern Recognition ,vol.14,pp 941-962,2000.
  45. Thyagaraju.GS,U.P.Kulkarni,”Modelling Of User Preferences in Single and Multiuser Context Aware Environments For Interactive Context Aware TV”, International Journal of Information Technology and Information Engineering ,IJITIE ,ISSN 0974 -4959, Volume 01 ,Issue No 01 , January 2011 – March 2011.
  46. Thyagaraju.GS,U.P.Kulkarni ,” Interactive Democratic Group Preference Algorithm for Interactive Context Aware TV”, 2010 IEEE International Conference on Computational Intelligence and Computing Research ,
  47. Thyagaraju.GS, U.P.Kulkarni” Modeling User Context for Interactive Context Aware TV”, 2010 IEEE International Conference on Computational Intelligence and Computing Research .
  48. Thyagaraju.GS,U.P.Kulkarni “Conflict Resolving Algorithms to Resolve Conflict in Multi-user Context-Aware Environments”, 2009 IEEE International Advance Computing Conference (IACC 2009)Patiala, India, 6-7 March 2009 , 978-1T-4244-1888-6/08/
  49. Thyagaraju.GS , UP Kulkarni ,”Conflict Resolution in multiuser context aware environment”, April /June 2008, ,” IEEE Pervasive Journal volume = {7}, number = {2}, issn = {1536-1268}, year = {2008}, (Impact Factor 2.293).
Index Terms

Computer Science
Information Sciences


Ubiquitous context recommendation engine conflict context aware tv family preference role age social status favorite program automatic fuzzy logic mood activity