Interactive Democratic Group Preference Algorithm for Interactive Context Aware TV

Thyagaraju, G S; Kulkarni, Umakant P.

doi:10.1109/iccic.2010.5705880

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…In other studies, Mast hoff discussed various selection strategies based on social theory and found that users were not dedicated to a specific selection method, but may use different strategies in selecting programs. The universal controller, proposed by Yoon and Woo, allowed users to intuitively control a smart TV as a group, but it required users to be actively involved in the control (Papgelis & Plexousakis, 2005;Sandvig, Mobasher, & Burke, 2008;Gong, 2010;Yu, Zhou, Hao, & Gu, 2006;Thyagaraju & Kulkarni, 2010a, 2011b.…”

Section: Related Workmentioning

confidence: 99%

Rough Set Theory Based User Aware TV Program and Settings Recommender

Thyagaraju¹,

Kulkarni²

2012

International Journal of Advanced Pervasive and Ubiquitous Computing

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In this paper the authors 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 utilizes 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 fuzzy logic and Rough Sets Based Decision Rules. 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. They have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system for small families.

show abstract

Section: Related Workmentioning

confidence: 99%

Rough Set Theory Based User Aware TV Program and Settings Recommender

Thyagaraju¹,

Kulkarni²

2012

International Journal of Advanced Pervasive and Ubiquitous Computing

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show abstract

“…Others: they are proposed algorithms that could not be classified into any category above (e.g. "conflict resolution" (Thyagaraju and Kulkarni, 2010)). …”

mentioning

confidence: 99%

A literature review of recommender systems in the television domain

Véras

Prota

Bispo

et al. 2015

Expert Systems with Applications

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“…In other studies, Mast hoff discussed various selection strategies based on social theory and found that users were not dedicated to a specific selection method, but may use different strategies in selecting programs. The universal controller, proposed by Yoon and Woo, allowed users to intuitively control a smart TV as a group, but it required users to be actively involved in the control [16,20,23,30,46,47]. Choonsung Shin and Woontack Woo proposed a series of socially aware program recommender for multiple viewers of digital TV using different concepts like linear selection method to Bayesian.…”

Section: Marilyn (-Multimodal Avatar Responsive Live Newsmentioning

confidence: 99%

“…[46,47,48,49].There have been studies to combine the fuzzy system and Bayesian networks .Yang proposed fuzzy Bayesian approach that estimates the density function from the conditional probabilities of the fuzzy supported values in order to use continuous value in Bayesian framework. Pan and Liu proposed fuzzy Bayesian network and inference algorithm using virtual nodes and Gaussian functions .However these methods have constraints where observed information should be only one and membership degree sum should be one Our proposed model includes simple and effective fuzzy Bayesian network without those constraints.…”

Section: P(service/context) = ----(18)mentioning

confidence: 99%

Family Aware TV Program and Settings Recommender

Gs¹,

Kulkarni²

2011

IJCA

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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.

show abstract

Interactive Democratic Group Preference Algorithm for Interactive Context Aware TV

Cited by 4 publications

References 14 publications

Rough Set Theory Based User Aware TV Program and Settings Recommender

Rough Set Theory Based User Aware TV Program and Settings Recommender

A literature review of recommender systems in the television domain

Family Aware TV Program and Settings Recommender

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