Publication
Tag Suggestion on YouTube by Personalizing Content-based Auto-Annotation
Dominik Henter; Damian Borth; Adrian Ulges
In: Proceedings of the International Conference on Multimedia. International ACM Workshop on Crowdsourcing for Multimedia (CrowdMM-2012), located at ACM Multimedia 2012, October 29, Nara, Japan, ACM, 10/2012.
Abstract
We address the challenge of tag recommendation for web video clips on portals such as YouTube. In a quantitative study on 23,000 YouTube videos, we first evaluate different tag suggestion strategies employing user profiling (using tags from the user’s upload history) as well as social signals (the channels a user subscribed to) and content analysis. Our results confirm earlier findings that – at least when employ- ing users’ original tags as ground truth – a history-based approach outperforms other techniques.
Second, we suggest a novel approach that integrates the strengths of history-based tag suggestion with a content matching crowd-sourced from a large repository of user gen- erated videos. Our approach performs a visual similarity matching and merges neighbors found in a large-scale ref- erence dataset of user-tagged content with others from the user’s personal history. This way, signals gained by crowd- sourcing can help to disambiguate tag suggestions, for ex- ample in cases of heterogeneous user interest profiles or non- existing user history. Our quantitative experiments indicate that such a personalized tag transfer gives strong improve- ments over a standard content matching, and moderate ones over a content-free history-based ranking.