|Title||Ranking Function Adaptation With Boosting Trees|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Keke Chen, Jing Bai, Zhaohui Zheng|
|Journal||ACM Transactions on Information Systems|
|Keywords||boosting regression trees, domain adaptation, learning to rank, user feedback, web search ranking|
Machine learned ranking functions have shown successes in web search engines. With the increasing demands on developing effective ranking functions for different search domains, we have seen a big bottleneck, i.e., the problem of insufﬁcient labeled training data, which has signiﬁcantly slowed the development and deployment of machine learned ranking functions for different domains. There are two possible approaches to address this problem: (1) combining labeled training data from similar domains with the small targetdomain labeled data for training or (2) using pairwise preference data extracted from user clickthrough log for the target domain for training. In this paper, we propose a new approach called tree based ranking function adaptation ('Trada') to effectively utilize these data sources for training cross-domain ranking functions. Tree adaptation assumes that ranking functions are trained with the Stochastic Gradient Boosting Trees method − a gradient boosting method on regression trees. It takes such a ranking function from one domain and tunes its tree based structure with a small amount of training data from the target domain. The unique features include (1) it can automatically identify the part of model that needs adjustment for the new domain, (2) it can appropriately weigh training examples considering both local and global distributions. Based on a novel pairwise loss function that we developed for pairwise learning, the basic tree adaptation algorithm is also extended ('Pairwise Trada') to utilize the pairwise preference data from the target domain to further improve the effectiveness of adaptation. Experiments are performed on real datasets to show that tree adaptation can provide better-quality ranking functions for a new domain than other methods.
|Full Text|| |
Keke Chen, Jing Bai, and Zhaohui Zheng,'Ranking Function Adaptation with Boosting Trees,' ACM Transactions on Information Systems (TOIS), accepted in July, 2011.