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. © 2012 Springer-Verlag.
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CITATION STYLE
Chelaru, S. V., Orellana-Rodriguez, C., & Altingovde, I. S. (2012). Can social features help learning to rank YouTube videos? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7651 LNCS, pp. 552–566). https://doi.org/10.1007/978-3-642-35063-4_40
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