Tag ranking

By Dong Liu, Xian-Sheng Hua, Linjun Yang, Meng Wang, HongJiang Zhang. 2009

497 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Computing (2021, background): “While this prior research indicates the capability for affective computing for emotional states, assessing these states as a condition of the experience of the built environment was deemed to be quite new.”
Understanding web images by object relation network (2012, background): “Tag ranking [15, 28] identifies the most relevant semantics among existing tags.”
Image ranking based on user browsing behavior (2012, methodology): “[21] use a random walk algorithm for tag ranking in Flickr, to overcome the sparsity problem of the tags associated to the images inside the social network.”
Classifying tag relevance with relevant positive and negative examples (2013, methodology): “We compare with the following five present day methods: tag position [10], tag ranking [8], neighbor voting [6], semantic field [12], and linear SVMs [3].”
Content-based visual search learned from social media (2012, methodology): “A number of methods have been proposed to attack the tag relevance estimation problem [20, 56, 64, 74, 75, 112, 138, 149, 151].”
Comment-based multi-view clustering of web 2.0 items (2014, methodology): “Approaches might use the top-ranked words as tags directly, or use the values in H as weights into a more sophisticated tag generation algorithm [31].”
Geo-semantic segmentation (2015, background): “Very similar to [6] and [15], each random walk iteration will update the scores matrix using the following equation:”