Model Adaptation via Model Interpolation and Boosting for Web Search Ranking

  title={Model Adaptation via Model Interpolation and Boosting for Web Search Ranking},
  author={Jianfeng Gao and Qiang Wu and C. Burges and K. Svore and Yi Su and N. Khan and Shalin S Shah and Hongyan Zhou},
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very different from the training data. The tree-based boosting algorithm achieves the best performance on most of the closed test sets where the test data and the training data are similar, but… Expand
Adapting boosting for information retrieval measures
This work presents a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, and shows how to find the optimal linear combination for any two rankers, and uses this method to solve the line search problem exactly during boosting. Expand
Multi-task learning for boosting with application to web search ranking
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing the specifics ofExpand
Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking
A novel adaptation algorithm is proposed, Pairwise-Trada, which is able to adapt ranking models that are trained with multi-grade labeled training data to the target market using the target-market-specific pair-wise preference data. Expand
Ranking function adaptation with boosting trees
A new approach called tree-based ranking function adaptation (Trada) is proposed to effectively utilize data sources for training cross-domain ranking functions and is extended to utilize the pairwise preference data from the target domain to further improve the effectiveness of adaptation. Expand
Personalized ranking model adaptation for web search
This paper proposes a general ranking model adaptation framework for personalized search that quickly learns to apply a series of linear transformations over the parameters of the given global ranking model such that the adapted model can better fit each individual user's search preferences. Expand
A Novel Framework for Ranking Model Adaptation
  • Peng Cai, Aoying Zhou
  • Computer Science
  • 2010 Seventh Web Information Systems and Applications Conference
  • 2010
This paper proposes a novel framework which extends the previous work using a listwise ranking algorithm for ranking adaptation, and estimates the importance weight of a query in the source domain and incorporates it into the state-of-the-art listwiseranking algorithm, known as AdaRank. Expand
Flexible sample selection strategies for transfer learning in ranking
A flexible transfer learning strategy based on sample selection that allows many existing supervised rankers to be adapted to the transfer learning setting and gives robust improvements. Expand
Multi-task learning to rank for web search
A boosting framework for learning to rank in the multi-task learning context is proposed to learn non-parametric common structures adaptively from multiple tasks in a stage-wise way to attack the problem of poor quality training data in web search. Expand
Adapting deep RankNet for personalized search
This paper continue-trained a variety of RankNets with different number of hidden layers and network structures over a previously trained global RankNet model, and observed that a deep neural network with five hidden layers gives the best performance. Expand
Web-Search Ranking with Initialized Gradient Boosted Regression Trees
This paper investigates Random Forests as a low-cost alternative algorithm to Gradient Boosted Regression Trees (GBRT) (the de facto standard of web-search ranking) and provides an upper bound of the Expected Reciprocal Rank (Chapelle et al., 2009) in terms of classification error. Expand


Ranking, Boosting, and Model Adaptation
We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaR ank, which has been shown to be empirically optimal for a widely usedExpand
Stochastic gradient boosting
Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration. TheExpand
Greedy function approximation: A gradient boosting machine.
Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansionsExpand
Learning to rank using gradient descent
RankNet is introduced, an implementation of these ideas using a neural network to model the underlying ranking function, and test results on toy data and on data from a commercial internet search engine are presented. Expand
Two Algorithms for Transfer Learning
Transfer learning aims at improving the performance on a target task given some degree of learning on one or more source tasks. This chapter introduces two transfer learning algorithms that can beExpand
An empirical study on language model adaptation
This article presents an empirical study of four techniques for adapting language models, including a maximum a posteriori (MAP) method and three discriminative training models, in the application of Japanese Kana-Kanji conversion, and tries to interpret the results in terms of the character error rate (CER) by correlating them with the characteristics of the adaptation domain, measured by using the information-theoretic notion of cross entropy. Expand
Language Model Adaptation with MAP Estimation and the Perceptron Algorithm
It is demonstrated that, in a multi-pass recognition scenario, it is better to use the perceptron algorithm on early pass word lattices, since the improved error rate improves acoustic model adaptation. Expand
Discriminative Reranking for Natural Language Parsing
The boosting approach to ranking problems described in Freund et al. (1998) is applied to parsing the Wall Street Journal treebank, and it is argued that the method is an appealing alternative-in terms of both simplicity and efficiency-to work on feature selection methods within log-linear (maximum-entropy) models. Expand
IR evaluation methods for retrieving highly relevant documents
The novel evaluation methods and the case demonstrate that non-dichotomous relevance assessments are applicable in IR experiments, may reveal interesting phenomena, and allow harder testing of IR methods. Expand
Random Forests
  • L. Breiman
  • Mathematics, Computer Science
  • Machine Learning
  • 2004
Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression. Expand