Can social features help learning to rank YouTube videos?

Date

2012-11-26

Author

Chelaru, Sergiu Viorel
Orellana-Rodriguez, Claudia
Altıngövde, İsmail Sengör

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Feature selection, Random forest, Social feature, Video retrieval, Normalize discount cumulative gain

URI

https://hdl.handle.net/11511/36174

DOI

https://doi.org/10.1007/978-3-642-35063-4_40

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

S. V. Chelaru, C. Orellana-Rodriguez, and İ. S. Altıngövde, “Can social features help learning to rank YouTube videos?,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36174.