An adaptive algorithm for learning changes in user interests
Proceedings of the eighth international conference on Information and knowledge management
Information Retrieval
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
TV3P: an adaptive assistant for personalized TV
IEEE Transactions on Consumer Electronics
Dynamic gain estimation in ambient media services
SAME '08 Proceedings of the 1st ACM international workshop on Semantic ambient media experiences
iMuseum: A scalable context-aware intelligent museum system
Computer Communications
A framework for human-centered provisioning of ambient media services
Multimedia Tools and Applications
Context-Dependent task computing in pervasive environment
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
| Hi-index | 0.00 |
Pervasive computing environment and users' demand for multimedia personalization precipitate a need for personalization tools to help people access desired multimedia content at anytime, anywhere, through any devices. User preference learning plays an important role in multimedia personalization. In this paper, we propose a learning approach to acquire and update user preference for multimedia personalization in pervasive computing environment. The approach is based on Master-Slave architecture, of which master device is a device with strong capabilities, such as PC, TV with STB (set-on-box) or PDR (Personal Digital Recorder), etc, and slave devices are pervasive terminals with limited resources. The preference learning and update is done in the master device by utilizing overall user feedback information collected from different devices as opposed to other traditional learning methods that just use partial feedback information in one device. The slave devices are responsible for observing user behavior and uploading feedback information to the master device. The master device is designed to support multiple learning methods: explicit input/modification and implicit learning. The implicit user preference learning algorithm, which applies relevance feedback and Naïve Bayes classifier approach, is described in detail.