Query-biased learning to rank for real-time twitter search
Query-biased learning to rank for real-time twitter search
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DOI：
10.1145/2396761.2398543
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Xin Zhang;Ben He;Tiejian Luo;Baobin Li
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文献类型：
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作者：
Xin Zhang;Ben He;Tiejian Luo;Baobin Li
通过整合不同的相关性证据来源，学习排名已被广泛应用于实时Twitter搜索，用户对新鲜的相关消息感兴趣。这种方法通常依赖于一组训练查询来学习一般的排名模型，我们认为，由于忽略了给定目标查询特有的特征和方面，因此学习排名所带来的好处可能没有被充分利用。在本文中，我们建议通过考虑查询之间的差异，进一步提高学习排名在实时Twitter搜索中的检索性能。具体地说，我们使用半监督转导学习算法来学习基于查询的排序模型，从而利用查询特定的特征，例如唯一扩展项来捕获目标查询的特征。该偏向查询的排序模型与通用排序模型相结合，以响应于给定的目标查询产生推文的最终排序列表。在标准TREC Tweets11集合上的广泛实验表明，我们提出的基于查询的学习排名方法的性能优于强基线，即传统的最新学习应用于排名算法。
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.