Query-biased learning to rank for real-time twitter search

  • Authors:

  • Xin Zhang;Ben He;Tiejian Luo;Baobin Li

  • Affiliations:

  • Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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Abstract

By incorporating diverse sources of evidence of relevance, learning to rank has been widely applied to real-time Twitter search, where users are interested in fresh relevant messages. Such approaches usually rely on a set of training queries to learn a general ranking model, which we believe that the benefits brought by learning to rank may not have been fully exploited as the characteristics and aspects unique to the given target queries are ignored. In this paper, we propose to further improve the retrieval performance of learning to rank for real-time Twitter search, by taking the difference between queries into consideration. In particular, we learn a query-biased ranking model with a semi-supervised transductive learning algorithm so that the query-specific features, e.g. the unique expansion terms, are utilized to capture the characteristics of the target query. This query-biased ranking model is combined with the general ranking model to produce the final ranked list of tweets in response to the given target query. Extensive experiments on the standard TREC Tweets11 collection show that our proposed query-biased learning to rank approach outperforms strong baseline, namely the conventional application of the state-of-the-art learning to rank algorithms.