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Presentation
A Regression Framework for Learning Ranking Functions using Relative Relevance Judgments
Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
  • Zhaohui Zheng
  • Hongyuan Zha
  • Keke Chen, Wright State University - Main Campus
  • Gordon Sun
Document Type
Conference Proceeding
Publication Date
7-1-2007
Abstract

Effective ranking functions are an essential part of commercial search engines. We focus on developing a regression framework for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish two types of relevance judgments used as the training data: 1) absolute relevance judgments arising from explicit labeling of search results; and 2) relative relevance judgments extracted from user clickthroughs of search results or converted from the absolute relevance judgments. We propose a novel optimization framework emphasizing the use of relative relevance judgments. The main contribution is the development of an algorithm based on regression that can be applied to objective functions involving preference data, i.e., data indicating that a document is more relevant than another with respect to a query. Experimental results are carried out using data sets obtained from a commercial search engine. Our results show significant improvements of our proposed methods over some existing methods.

Comments

Presented at the 30th International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, July 23-27, 2007.

Citation Information
Zhaohui Zheng, Hongyuan Zha, Keke Chen and Gordon Sun. "A Regression Framework for Learning Ranking Functions using Relative Relevance Judgments" Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2007) p. 287 - 294
Available at: http://works.bepress.com/keke_chen/31/