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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
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
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.
Recommendation of the Committee of Ministers to member states on the protection and promotion of the universality, integrity and openness of the Internet (Adopted by the Committee of Ministers on 21 September 2011 at the 1121st meeting of the Ministers' Deputies)
twittteruser recommendation system based on algorithms We've found that the power of suggestion can be a great thing to help people get started, but it's important that we suggest things relevant to them. We've created a number of algorithms to identify users across a variety of clusters who tweet actively and are engaged with their audiences. These new algorithms help us group these active users into lists of users by interests. Rather than suggesting a random set of 20 users for a new user to follow, now we let users browse into the areas they are interested in and choose who they want to follow from these lists. These lists will be refreshed frequently as the algorithms identify new users who should be suggested in these lists and some that are not as engaging to new users will be removed
X. Zhang, X. Xin, D. Li, W. Liu, P. Ren, Z. Chen, J. Ma, and Z. Ren. Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, page 231–239. New York, NY, USA, Association for Computing Machinery, (Feb 27, 2023)
X. Li, F. Yan, X. Zhao, Y. Wang, B. Chen, H. Guo, and R. Tang. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, page 1268--1277. (2023)
Y. Chen, M. Yang, Y. Zhang, M. Zhao, Z. Meng, J. Hao, and I. King. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, page 94–102. New York, NY, USA, Association for Computing Machinery, (Feb 15, 2022)