  Tag recommendation is a common way to enrich the textual annotation of
multimedia contents. However, state-of-the-art recommendation methods are built
upon the pair-wised tag relevance, which hardly capture the context of the web
video, i.e., when who are doing what at where. In this paper we propose the
context-oriented tag recommendation (CtextR) approach, which expands tags for
web videos under the context-consistent constraint. Given a web video, CtextR
first collects the multi-form WWW resources describing the same event with the
video, which produce an informative and consistent context; and then, the tag
recommendation is conducted based on the obtained context. Experiments on an
80,031 web video collection show CtextR recommends various relevant tags to web
videos. Moreover, the enriched tags improve the performance of web video
categorization.
