Query dependent ranking using K-nearest neighbor

Many ranking models have been proposed in information retrieval, and recently machine learning techniques have also been applied to ranking model construction. Most of the existing methods do not take into consideration the fact that significant differences exist between queries, and only resort to a single function in ranking of documents. In this paper, we argue that it is necessary to employ different ranking models for different queries and conduct what we call query-dependent ranking. As the first such attempt, we propose a K-Nearest Neighbor (KNN) method for querydependent ranking. We first consider an online method which creates a ranking model for a given query by using the labeled neighbors of the query in the query feature space and then rank the documents with respect to the query using the created model. Next, we give two offline approximations of the method, which create the ranking models in advance to enhance the efficiency of ranking. And we prove a theory which indica...

• Type: Journal
• Year: 2008
• Where: SIGIR
• Authors: Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li, Heung-Yeung Shum
• Keywords: Information Technology, Many Ranking Models, Ranking, Ranking Models, SIGIR 2008