Online diversified ranking algorithm based on clustering and user clicks

被引：0
|
作者：
Ma Q.-L. [1 ]
Lin G.-L. [1 ]
机构：
[1] School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2011年 / 39卷 / 12期
关键词：
Clustering; Diversification; Information retrieval; Online ranking; Ranking algorithm;
D O I：
10.3969/j.issn.1000-565X.2011.12.012
中图分类号：
学科分类号：
摘要：
In the information retrieval on the Internet, diversified ranking methods are used to provide top diverse results for users. This paper proposes an online diversified ranking algorithm CRBA based on clustering and user clicks. CRBA utilizes the similarity of documents to user feedbacks and provides diverse ranking results according to the continuous interaction of users. With the combination of the online method and the offline one, CRBA takes advantage of the topic clustering so that the convergence can be speeded up by preliminarily dividing candidate documents according to their topics. Moreover, it utilizes the merits of online ranking algorithms so that more accurate and complete estimation of users' purposes can be obtained from user clicks. Experimental results show that, as compared with the other online diversified ranking algorithms, CRBA converges more quickly and adapts well to the ranking of documents with a large amount.
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页码：70 / 74+99
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