TY - JOUR
T1 - User preference learning for multimedia personalization in pervasive computing environment
AU - Yu, Zhiwen
AU - Zhang, Daqing
AU - Zhou, Xingshe
AU - Li, Changde
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33745308627&partnerID=8YFLogxK
U2 - 10.1007/11552451_32
DO - 10.1007/11552451_32
M3 - 会议文章
AN - SCOPUS:33745308627
SN - 0302-9743
VL - 3682 LNAI
SP - 236
EP - 242
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Y2 - 14 September 2005 through 16 September 2005
ER -