Recently learning to rank has been widely used in real-time Twitter Search by integrating various of evidence of relevance and recency features into together. In real-time Twitter search, whereby the information need of a user is represented by a query at a specific time, users are interested in fresh messages. In this paper, we introduce a new ranking strategy to rerank the tweets by incorporating multiple features. Besides, an empirical study of learning to rank for real-time Twitter search is conducted by adopting the state-of-the-art learning to rank approaches. Experiments on the standard TREC Tweets11 collection show that both the listwise and pairwise learning to rank methods outperform baselines, namely the content-based retrieval models. © 2013 Springer-Verlag.
CITATION STYLE
Cheng, F., Zhang, X., He, B., Luo, T., & Wang, W. (2013). A survey of learning to rank for real-time Twitter search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7719 LNCS, pp. 150–164). https://doi.org/10.1007/978-3-642-37015-1_13
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