Context-Oriented Web Video Tag Recommendation

TL;DR

This paper introduces CtextR, a context-oriented web video tag recommendation method that leverages multi-source web resources to improve tag relevance and enhance video categorization accuracy.

Contribution

It proposes a novel context-aware approach for web video tag recommendation that utilizes multi-form web resources to generate more relevant tags.

Findings

CtextR effectively recommends relevant tags for web videos.

Enriched tags improve web video categorization performance.

Experiments on 80,031 videos validate the approach.

Abstract

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.