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System and Method for Learning Balanced Relevance Functions from Expert and User Judgments
Kno.e.sis Publications
  • Keke Chen, Wright State University - Main Campus
  • Ya Zhang
  • Zhaohui Zheng
  • Hongyuan Zha
  • Gordon Sun
Document Type
Patent
Publication Date
12-4-2008
Abstract
The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion.
Comments

United States Patent #2008/0301069 A1

Citation Information
Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, et al.. "System and Method for Learning Balanced Relevance Functions from Expert and User Judgments" (2008)
Available at: http://works.bepress.com/keke_chen/40/