Intelligent media indexing and television recommender systems

Authors:
Tamas Jambor
Affiliations:
Department of Computer Science, University College London
Venue:
FDIA'09 Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access
Year:
2009

Citing 10
Cited 0

An algorithmic framework for performing collaborative filtering

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Incorporating contextual information in recommender systems using a multidimensional approach

ACM Transactions on Information Systems (TOIS)
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
The Long Tail: Why the Future of Business Is Selling Less of More

The Long Tail: Why the Future of Business Is Selling Less of More
Personalization on a peer-to-peer television system

Multimedia Tools and Applications
Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system

Multimedia Tools and Applications
Organizing multimedia data socially

CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
An empirical study on effectiveness of temporal information as implicit ratings

Expert Systems with Applications: An International Journal
Natural language processing for usage based indexing of web resources

ECIR'07 Proceedings of the 29th European conference on IR research

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a state-of-art review on recommender systems that has been identified as possible areas of my research. It describes the current generation of collaborative filtering methods which are usually classified into three main categories: item-based, user-based and hybrid methods. The paper considers these methods to be applied to digital television providing recommendation for viewers. Personalised television is predicted to be the next step in the evolution of television which might reshape the whole landscape of mass media. The paper also identifies anticipated problems in the domain of recommender systems which includes indexing, collaborative filtering, ranking problems and possible research directions to solve these problems. Finally, these challenges are considered in the domain of personalised television which has its own inherent shortcomings.