The personal electronic program guide—towards the pre-selection of individual TV programs
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Web page classification based on k-nearest neighbor approach
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
PTV: Intelligent Personalised TV Guides
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Text categorization based on k-nearest neighbor approach for web site classification
Information Processing and Management: an International Journal
The Development and Prospect of Personalized TV Program Recommendation Systems
MSE '02 Proceedings of the Fourth IEEE International Symposium on Multimedia Software Engineering
Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
An adaptive k-nearest neighbor text categorization strategy
ACM Transactions on Asian Language Information Processing (TALIP)
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Image Denoising with k-nearest Neighbor and Support Vector Regression
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
A Weighted k-Nearest Neighbor Method for Gene Ontology Based Protein Function Prediction
IMSCCS '07 Proceedings of the Second International Multi-Symposiums on Computer and Computational Sciences
k-means discriminant maps for data visualization and classification
Proceedings of the 2008 ACM symposium on Applied computing
Efficient Privacy-Preserving k-Nearest Neighbor Search
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
A personalized recommendation system for electronic program guide
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
TV3P: an adaptive assistant for personalized TV
IEEE Transactions on Consumer Electronics
Development of a TV reception navigation system personalized with viewing habits
IEEE Transactions on Consumer Electronics
Social TV: The impact of social awareness on content navigation within IPTV systems
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Dynamic adjustable multimedia streaming service architecture over cloud computing
Computer Communications
| Hi-index | 0.00 |
Digital TV channels require users to spend more time to choose their favorite TV programs. Electronic Program Guides (EPG) cannot be used to find popular TV programs. Hence, this paper proposes a personalized Digital Video Broadcasting -- Terrestrial(DVB-T) Digital TV program recommendation system for P2P social networks. From the DVB-T signal, we obtain EPG of TV programs. The frequency and duration of the programs that users have watched are used to extract programs that users are interested in. The information is collected and weighted by Information Retrieval (IR). The program information is then clustered by k-means. Clusters of users are also grouped by k-means to find cluster relationships. In each group, we decide the most popular program in the group according to the program weight of the channel. When a new user begins to watch the TV program, the K-Nearest Neighbor (kNN) classification method is used to determine the user's predicted cluster label. Then, our system recommends popular programs in the predicted cluster and similar clusters.