Query expansion techniques generally select new query terms from a set of top ranked documents . Although a user 's manual judgment of those documents would much help to select good expansion terms , it is difficult to get enough feedback from users in practical situations . In this paper we propose a query expansion technique which performs well even if a user notifies just a relevant document and a non-relevant document . In order to tackle this specific condition , we introduce two refinements to a well-known query expansion technique . One is application of a transductive learning technique in order to increase relevant documents . The other is a modified parameter estimation method which laps the predictions by multiple learning trials and try to differentiate the importance of candidate terms for expansion in relevant documents . Experimental results show that our technique outperforms some traditional query expansion methods in several evaluation measures . Query expansion is a simple but very useful technique to improve search performance by adding some terms to an initial query . While many query expansion techniques have been proposed so far , a standard method of performing is to use relevance information from a user ( Ruthven , 2003 ) . If we can use more relevant documents in query expansion , the likelihood of selecting query terms achieving high search improvement increases . However it is impractical to expect enough relevance information . Some researchers said that a user usually notifies few relevance feedback or nothing ( Dumais and et al . , 2003 ) . In this paper we investigate the potential performance of query expansion under the condition that we can utilize little relevance information , especially we only know a relevant document and a nonrelevant document . To overcome the lack of relevance information , we tentatively increase the number of relevant documents by a machine learning technique called Transductive Learning . Compared with ordinal inductive learning approach , this learning technique works even if there is few training examples . In our case , we can use many documents in a hit-list , however we know the relevancy of few documents . When applying query expansion , we use those increased documents as if they were true relevant ones . When applying the learning , there occurs some difficult problems of parameter settings . We also try to provide a reasonable resolution for the problems and show the effectiveness of our proposed method in experiments . The point of our query expansion method is that we focus on the availability of relevance information in practical situations . There are several researches which deal with this problem . Pseudo relevance feedback which assumes top n documents as relevant ones is one example . This method is simple and relatively effective if a search engine returns a hit-list which contains a certain number of relative documents in the upper part . However , unless this assumption holds , it usually gives a worse ranking than the initial search . Thus several researchers propose some specific procedure to make pseudo feedback be effective ( Yu and et al , 2003 ; Lam-Adesina and Jones , 2001 ) . In another way , Onoda ( Onoda et al . , 2004 ) tried to apply one-class SVM ( Support Vector Machine ) to relevance feedback . Their purpose is to improve search performance by using only nonrelevant documents . Though their motivation is similar to ours in terms of applying a machine learning method to complement the lack of relevance information , the assumption is somewhat different . Our assumption is to utilizes manual but the minimum relevance judgment . Transductive leaning has already been applied in the field of image retrieval ( He and et al . , 2004 ) . In this research , they proposed a transductive method called the manifold-ranking algorithm and showed its effectiveness by comparing with active learning based Support Vector Machine . However , their setting of relevance judgment is not different from many other traditional researches . They fix the total number of images that are marked by a user to 20 . As we have already claimed , this setting is not practical because most users feel that 20 is too much for judgment . We think none of research has not yet answered the question . For relevance judgment , most of the researches have adopted either of the following settings . One is the setting of " Enough relevant documents are available " , and the other is " No relevant document is available " . In contrast to them , we adopt the setting of " Only one relevant document is available " . Our aim is to achieve performance improvement with the minimum effort of judging relevancy of documents . The reminder of this paper is structured as follows . Section 2 describes two fundamental techniques for our query expansion method . Section 3 explains a technique to complement the smallness of manual relevance judgment . Section 4 introduces a whole procedure of our query expansion method step by step . Section 5 shows empirical evidence of the effectiveness of our method compared with two traditional query expansion methods . Section 6 investigates the experimental results more in detail . Finally , Section 7 summarizes our findings . In this paper we proposed a novel query expansion method which only use the minimum manual judgment . To complement the lack of relevant documents , this method utilizes the SGT transductive learning algorithm to predict the relevancy of unjudged documents . Since the performance of SGT much depends on an estimation of the fraction of relevant documents , we propose a method to sample some good fraction values . We also propose a method to laps the predictions of multiple SGT trials with above sampled fraction values and try to differentiate the importance of candidate terms for expansion in relevant documents . The experimental results showed our method outperforms other query expansion methods in the evaluations of several criteria .

Challenge: Although existing works on query expansion assume some relevant documents at their disposal, it is impractical because users provide few or no feedback.
Approach: They consider an experimental setup where there is only one relevant document provided by a user and extend a transductive learning technique to compensate.
Outcome: The proposed technique outperforms traditional query expansion methods in several evaluation metrics.