Clustering Search Results of Non-text User Generated Content
Pan Juasiripukdee, Lisa Wiyartanti, Laehyun Kim
International Journal of Information Studies, Vol. 3, No. 1, 2011.
DOI: 
Link: https://www.dline.info/ijis/fulltext/v3n1/ijisv3n1_5.pdf
Abstract:
Non-text user generated content (UGC), such as videos and images, is usually searched by metadata.
Metadata, such as title, tags, and description, is created by users whenever content is uploaded. However, in many cases
metadata can have multiple meanings. This requires users to spend time sifting through a long list of search results until
they can find all the content for which they were actually looking. In order to address this limitation, we suggest an
algorithm to cluster search results using keyword similarity. Clustering search results from YouTube are accomplished by
using the Markov clustering algorithm, which helps users to quickly and easily find what they want. Finally, we conclude
by evaluating the performance results of our clustering algorithm.