Can social features help learning to rank youtube videos?

Authors:
Sergiu Viorel Chelaru;Claudia Orellana-Rodriguez;Ismail Sengor Altingovde
Affiliations:
L3S Research Center, Hannover, Germany;L3S Research Center, Hannover, Germany;L3S Research Center, Hannover, Germany
Venue:
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Year:
2012

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Abstract

We investigate the impact of social features (such as likes, dislikes, comments, etc.) on the effectiveness of video retrieval in YouTube video sharing system using state-of-the-art learning to rank approaches and a greedy feature selection algorithm. Our experiments based on a dataset of 3,500 annotated query-video pairs reveal that social features are promising to improve the retrieval performance.