Family Aware TV Program and Settings Recommender

Gs, Thyagaraju; Kulkarni, Umakant P.

doi:10.5120/3556-4889

IJCA

2011

DOI: 10.5120/3556-4889

|View full text |Cite

Family Aware TV Program and Settings Recommender

Thyagaraju Gs¹,

Umakant P. Kulkarni²

Abstract: 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 moti… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2012

2017

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Vildjiounaite

Gimel’farb

Kyllönen

et al. 2015

The Scientific World Journal

View full text Add to dashboard Cite

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design.

show abstract

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Vildjiounaite

Gimel’farb

Kyllönen

et al. 2015

The Scientific World Journal

View full text Add to dashboard Cite

show abstract

Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

Greenberg

2017

Journal of Data and Information Science

View full text Add to dashboard Cite

Purpose: The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research. This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science.Design/methodology/approach: This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science. Findings:The "utilitarian nature" and "historical and traditional views" of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part of a metadata lingua franca to help frame research in the data science research space. Research limitations:There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore. Practical implications:The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem.Originality/value: Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.

show abstract

Rough Set Theory Based User Aware TV Program and Settings Recommender

Thyagaraju¹,

Kulkarni²

2012

International Journal of Advanced Pervasive and Ubiquitous Computing

View full text Add to dashboard Cite

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

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Family Aware TV Program and Settings Recommender

Cited by 3 publications

References 30 publications

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

Rough Set Theory Based User Aware TV Program and Settings Recommender

Contact Info

Product

Resources

About