On the Robustness of Document Re-Ranking Techniques: A Comparison of Label Propagation, KNN, and Relevance Feedback
Yuen‐Hsien Tseng, Chen‐Yang Tsai, Ze-Jing Chuang · 2007
This paper describes our work at the sixth NTCIR workshop on the subtask of C-C single language information retrieval. We compared label propagation (LP), K-nearest neighboring (KNN), and relevance feedback (RF) for document re-ranking and found that RF is a more robust technique for performance improvement, while LP and KNN are sensitive to the choice and the number of relevant documents for successful document re-ranking. Keywords: Chinese IR, relevance feedback, unsupervised clustering, semi-supervised categorization.