Machine Learning - Special issue on inductive transfer
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
A generic ranking function discovery framework by genetic programming for information retrieval
Information Processing and Management: an International Journal
Information Retrieval
Integrating word relationships into language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
TransRank: A Novel Algorithm for Transfer of Rank Learning
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Word sense disambiguation with distribution estimation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning to rank only using training data from related domain
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Multi-task learning for boosting with application to web search ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Query weighting for ranking model adaptation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Cross-language information retrieval with latent topic models trained on a comparable corpus
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Learning research in knowledge transfer
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
Personalized ranking model adaptation for web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 22nd international conference on World Wide Web
Transferring knowledge with source selection to learn IR functions on unlabeled collections
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Adapting deep RankNet for personalized search
Proceedings of the 7th ACM international conference on Web search and data mining
Democracy is good for ranking: towards multi-view rank learning and adaptation in web search
Proceedings of the 7th ACM international conference on Web search and data mining
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Recently, learning to rank technology is attracting increasing attention from both academia and industry in the areas of machine learning and information retrieval. A number of algorithms have been proposed to rank documents according to the user-given query using a human-labeled training dataset. A basic assumption behind general learning to rank algorithms is that the training and test data are drawn from the same data distribution. However, this assumption does not always hold true in real world applications. For example, it can be violated when the labeled training data become outdated or originally come from another domain different from its counterpart of test data. Such situations bring a new problem, which we define as cross domain learning to rank. In this paper, we aim at improving the learning of a ranking model in target domain by leveraging knowledge from the outdated or out-of-domain data (both are referred to as source domain data). We first give a formal definition of the cross domain learning to rank problem. Following this, two novel methods are proposed to conduct knowledge transfer at feature level and instance level, respectively. These two methods both utilize Ranking SVM as the basic learner. In the experiments, we evaluate these two methods using data from benchmark datasets for document retrieval. The results show that the feature-level transfer method performs better with steady improvements over baseline approaches across different datasets, while the instance-level transfer method comes out with varying performance depending on the dataset used.