How useful is social feedback for learning to rank YouTube videos?

Date

2014-09-01

Author

CHELARU, Sergiu
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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A vast amount of social feedback expressed via ratings (i.e., likes and dislikes) and comments is available for the multimedia content shared through Web 2.0 platforms. However, the potential of such social features associated with shared content still remains unexplored in the context of information retrieval. In this paper, we first study the social features that are associated with the top-ranked videos retrieved from the YouTube video sharing site for the real user queries. Our analysis considers both raw and derived social features. Next, we investigate the effectiveness of each such feature for video retrieval and the correlation between the features. Finally, we investigate the impact of the social features on the video retrieval effectiveness using state-of-the-art learning to rank approaches. In order to identify the most effective features, we adopt a new feature selection strategy based on the Maximal Marginal Relevance (MMR) method, as well as utilizing an existing strategy. In our experiments, we treat popular and rare queries separately and annotate 4,969 and 4,949 query-video pairs from each query type, respectively. Our findings reveal that incorporating social features is a promising approach for improving the retrieval performance for both types of queries.

Subject Keywords

Computer Networks and Communications, Hardware and Architecture, Software

URI

https://hdl.handle.net/11511/40437

Journal

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS

DOI

https://doi.org/10.1007/s11280-013-0258-9

Collections

Department of Computer Engineering, Article

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Citation Formats

S. CHELARU, C. Orellana-Rodriguez, and İ. S. Altıngövde, “How useful is social feedback for learning to rank YouTube videos?,” WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, pp. 997–1025, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40437.