A survey of learning to rank for real-time twitter search

Authors:
Fuxing Cheng;Xin Zhang;Ben He;Tiejian Luo;Wenjie Wang
Affiliations:
Information Dynamic and Engineering Applications Laboratory, University of Chinese Academy of Sciences, China, Key Laboratory of Computional Geodynamics, University of Chinese Academy of Sciences, ...;Information Dynamic and Engineering Applications Laboratory, University of Chinese Academy of Sciences, China, Key Laboratory of Computional Geodynamics, University of Chinese Academy of Sciences, ...;Information Dynamic and Engineering Applications Laboratory, University of Chinese Academy of Sciences, China, Key Laboratory of Computional Geodynamics, University of Chinese Academy of Sciences, ...;Information Dynamic and Engineering Applications Laboratory, University of Chinese Academy of Sciences, China, Key Laboratory of Computional Geodynamics, University of Chinese Academy of Sciences, ...;Information Dynamic and Engineering Applications Laboratory, University of Chinese Academy of Sciences, China, Key Laboratory of Computional Geodynamics, University of Chinese Academy of Sciences, ...
Venue:
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Year:
2012

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Abstract

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.