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

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.