Automatic text processing
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Charting past, present, and future research in ubiquitous computing
ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 1
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emotion Detection from Speech to Enrich Multimedia Content
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Using SMIL to encode interactive, peer-level multimedia annotations
Proceedings of the 2003 ACM symposium on Document engineering
The Family Video Archive: an annotation and browsing environment for home movies
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Interactive multimedia annotations: enriching and extending content
Proceedings of the 2004 ACM symposium on Document engineering
Is It Time for a Moratorium on Metadata?
IEEE MultiMedia
M4Note: A Multimodal Tool for Multimedia Annotations
LA-WEBMEDIA '04 Proceedings of the WebMedia & LA-Web 2004 Joint Conference 10th Brazilian Symposium on Multimedia and the Web 2nd Latin American Web Congress
IEEE Transactions on Knowledge and Data Engineering
Semantics, content, and structure of many for the creation of personal photo albums
Proceedings of the 15th international conference on Multimedia
Social Sharing of Television Content: An Architecture
ISMW '07 Proceedings of the Ninth IEEE International Symposium on Multimedia Workshops
Enhancing Multimodal Annotations with Pen-Based Information
ISMW '07 Proceedings of the Ninth IEEE International Symposium on Multimedia Workshops
Multimedia content personalization based on peer-level annotation
Proceedings of the seventh european conference on European interactive television conference
Journal of Artificial Intelligence Research
Video news classification for automatic content personalization: a genetic algorithm based approach
Proceedings of the 14th Brazilian Symposium on Multimedia and the Web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Affective video content representation and modeling
IEEE Transactions on Multimedia
Content-based movie analysis and indexing based on audiovisual cues
IEEE Transactions on Circuits and Systems for Video Technology
Information theory-based shot cut/fade detection and video summarization
IEEE Transactions on Circuits and Systems for Video Technology
Joint Key-Frame Extraction and Object Segmentation for Content-Based Video Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Creating peer-level video annotations for web-based multimedia
EGMM'04 Proceedings of the Seventh Eurographics conference on Multimedia
International Journal of Multimedia Data Engineering & Management
Personalized presentations from community assets
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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Peer-level annotation stands for the enrichment of content by any user, who acts as author, being able to make annotations, using, for instance, handwriting or speech recognition capabilities. This type of annotation makes users comfortable when taking digital notes, as they do in every day life. This is an advantage over hierarchical authoring, which is a time-consuming task usually employed by content providers. This paper proposes a content-based recommender architecture which explores information that is available at the time users enhance content. This feature enables our architecture to reach a certain level of semantic information from the content and from user's preferences, which is essential for recommender systems applications.