Online photo services such as Flickr and Zooomr allow users
to share their photos with family, friends, and the online
community at large. An important facet of these services
is that users manually annotate their photos using so called
tags, which describe the contents of the photo or provide
additional contextual and semantical information. In this
paper we investigate how we can assist users in the tagging
phase. The contribution of our research is twofold. We
analyse a representative snapshot of Flickr and present the
results by means of a tag characterisation focussing on how
users tags photos and what information is contained in the
tagging. Based on this analysis, we present and evaluate tag
recommendation strategies to support the user in the photo
annotation task by recommending a set of tags that can be
added to the photo. The results of the empirical evaluation
show that we can effectively recommend relevant tags for a
variety of photos with different levels of exhaustiveness of
original tagging.
We just presented yesterday at ISMIR a tutorial about Linked Data for music-related information. More information on the tutorial is available on the tutorial website, and the
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
E-travel is comprehensive framework for delivering personalized travel services using agent infrastructure based on our works in agent-related fields: Multimodal Communication Between Users and Software Agents and Modelling User on the Basis of Interactions with a WWW Based System. The system utilizes many existing up-to-day technologies related to the term of Semantic Web: JADE agent platform together with Jena semantic framework for processing ontology demarcated data. For easy and type safe access to ontology Jastor (Java beans generator from Web Ontologies (OWL)) has been used. Raccoon server provides a way to transform ontological data into browser-readable forms.
Our solution addresses set of the following problems:
* Connecting synchronous HTTP protocol with asynchronous nature of software agents.
* Web browser and mobile interface providing natural access to the multi-agent system.
* Separation of data and view thanks to: Model-View-Controller architecture, ontologies and Raccoon server for transforming data into view.
* Content personalization basen on user modelling, including: stereotyping, user profile learning and exploitation.
X. Zhang, X. Xin, D. Li, W. Liu, P. Ren, Z. Chen, J. Ma, и Z. Ren. Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, стр. 231–239. New York, NY, USA, Association for Computing Machinery, (27.02.2023)
H. Chen, Y. Li, S. Shi, S. Liu, H. Zhu, и Y. Zhang. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, стр. 75–84. New York, NY, USA, Association for Computing Machinery, (15.02.2022)
Y. Chen, M. Yang, Y. Zhang, M. Zhao, Z. Meng, J. Hao, и I. King. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, стр. 94–102. New York, NY, USA, Association for Computing Machinery, (15.02.2022)
S. Fujita, G. Dupret, и R. Baeza-Yates. (2012)cite arxiv:1204.2712Comment: 2nd International Workshop on Usage Analysis and the Web of Data (USEWOD2012) in the 21st International World Wide Web Conference (WWW2012), Lyon, France, April 17th, 2012.
Y. Su, R. Zhang, S. Erfani, и J. Gan. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, (июля 2021)
Q. Chen, H. Zhao, W. Li, P. Huang, и W. Ou. Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data, ACM, (августа 2019)
A. Zimdars, D. Chickering, и C. Meek. UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, стр. 580--588. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2001)
T. Bai, J. Nie, W. Zhao, Y. Zhu, P. Du, и J. Wen. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, ACM, (июня 2018)
B. Hao, J. Zhang, C. Li, H. Chen, и H. Yi. Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, (2020)