Helping people find what they don't know
Communications of the ACM
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A lattice-based approach to query-by-example spoken document retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Topic-bridged PLSA for cross-domain text classification
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th international conference on World wide web
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Heterogeneous cross domain ranking in latent space
Proceedings of the 18th ACM conference on Information and knowledge management
ACM Transactions on Computer-Human Interaction (TOCHI)
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
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
IEEE Transactions on Knowledge and Data Engineering
Domain adaptation meets active learning
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Design and implementation of relevance assessments using crowdsourcing
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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We introduce the problem of domain adaptation for content-based retrieval and propose a domain adaptation method based on relative aggregation points (RAPs). Content-based retrieval including image retrieval and spoken document retrieval enables a user to input examples as a query, and retrieves relevant data based on the similarity to the examples. However, input examples and relevant data can be dissimilar, especially when domains from which the user selects examples and from which the system retrieves data are different. In content-based geographic object retrieval, for example, suppose that a user who lives in Beijing visits Kyoto, Japan, and wants to search for relatively inexpensive restaurants serving popular local dishes by means of a content-based retrieval system. Since such restaurants in Beijing and Kyoto are dissimilar due to the difference in the average cost and areas' popular dishes, it is difficult to find relevant restaurants in Kyoto based on examples selected in Beijing. We propose a solution for this problem by assuming that RAPs in different domains correspond, which may be dissimilar but play the same role. A RAP is defined as the expectation of instances in a domain that are classified into a certain class, e.g. the most expensive restaurant, average restaurant, and restaurant serving the most popular dishes. Our proposed method constructs a new feature space based on RAPs estimated in each domain and bridges the domain difference for improving content-based retrieval in heterogeneous domains. To verify the effectiveness of our proposed method, we evaluated various methods with a test collection developed for content-based geographic object retrieval. Experimental results show that our proposed method achieved significant improvements over baseline methods. Moreover, we observed that the search performance of content-based retrieval in heterogeneous domains was significantly lower than that in homogeneous domains. This finding suggests that relevant data for the same search intent depend on the search context, that is, the location where the user searches and the domain from which the system retrieves data.