document: Learning to rank relational objects and its application to web search Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects . The technology has been successfully applied to web search , and is becoming one of the key machineries for building search engines . Existing approaches to learning to rank , however , did not consider the cases in which there exists relationship between the objects to be ranked , despite of the fact that such situations are very common in practice . For example , in web search , given a query certain relationships usually exist among the the retrieved documents , e.g. , URL hierarchy , similarity , etc. , and sometimes it is necessary to utilize the information in ranking of the documents . This paper addresses the issue and formulates it as a novel learning problem , referred to as , ` learning to rank relational objects ' . In the new learning task , the ranking model is defined as a function of not only the contents ( features ) of objects but also the relations between objects . The paper further focuses on one setting of the learning problem in which the way of using relation information is predetermined . It formalizes the learning task as an optimization problem in the setting . The paper then proposes a new method to perform the optimization task , particularly an implementation based on SVM . Experimental results show that the proposed method outperforms the baseline methods for two ranking tasks ( Pseudo Relevance Feedback and Topic Distillation ) in web search , indicating that the proposed method can indeed make effective use of relation information and content information in ranking .
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