Query expansion by spatial co-occurrence for image retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Tag-based social image search with visual-text joint hypergraph learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Integrating rich information for video recommendation with multi-task rank aggregation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Information Sciences: an International Journal
Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot
Information Sciences: an International Journal
Personalized ranking model adaptation for web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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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With the explosive emergence of vertical search domains, applying the broad-based ranking model directly to different domains is no longer desirable due to domain differences, while building a unique ranking model for each domain is both laborious for labeling data and time consuming for training models. In this paper, we address these difficulties by proposing a regularization-based algorithm called ranking adaptation SVM (RA-SVM), through which we can adapt an existing ranking model to a new domain, so that the amount of labeled data and the training cost is reduced while the performance is still guaranteed. Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains. In addition, we assume that documents similar in the domain-specific feature space should have consistent rankings, and add some constraints to control the margin and slack variables of RA-SVM adaptively. Finally, ranking adaptability measurement is proposed to quantitatively estimate if an existing ranking model can be adapted to a new domain. Experiments performed over Letor and two large scale data sets crawled from a commercial search engine demonstrate the applicabilities of the proposed ranking adaptation algorithms and the ranking adaptability measurement.