Ranking with Query Influence Weighting for document retrieval

• Published in: 2009 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 1177 - 1182
• Main Authors: Zhen Liao, Ya Lou Huang, Mao Qiang Xie, Jie Liu, Yang Wang, Min Lu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2009
• Subjects: Cybernetics; Document Retrieval; Learning to Rank; Machine learning; Query Influence Weighting; Ranking SVM
• ISBN: 9781424437023; 1424437024
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2009.5212411

Ranking continuously plays an important role in document retrieval and has attracted remarkable attentions. Existing ranking methods conduct the loss function for each query independently but ignore the fact that minimizing the loss of one query may increase that of another if they are contradictory. In principle, the punishment for errors of important queries should be enlarged. In this paper we propose a new approach ldquoQuery Influence Weightingrdquo, which adopts ldquoQuery Influence Weightingrdquo algorithm for computing query importance and incorporates the importance into the loss function for guiding the model constructing. We conduct a ranking model based on a state-of-art method named Ranking SVM. Experimental results on two public datasets show that the ldquoQuery Influence Weightingrdquo approach outperforms conventional Ranking SVM and other baselines. We further analyze the influence consistency on training and testing datasets and validate the effectiveness of our approach.