An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Building a Latent Semantic Index of an Image Database from Patterns of Relevance Feedback
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
The Journal of Machine Learning Research
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Email Surveillance Using Non-negative Matrix Factorization
Computational & Mathematical Organization Theory
Robust Scene Categorization by Learning Image Statistics in Context
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast collapsed gibbs sampling for latent dirichlet allocation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The aspect Bernoulli model: multiple causes of presences and absences
Pattern Analysis & Applications
Usefulness of quality click-through data for training
Proceedings of the 2009 workshop on Web Search Click Data
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Image annotation using clickthrough data
Proceedings of the ACM International Conference on Image and Video Retrieval
Understanding User-Web Interactions via Web Analytics
Understanding User-Web Interactions via Web Analytics
Visual-semantic graphs: using queries to reduce the semantic gap in web image retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
High-Dimensional Multimodal Distribution Embedding
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Using probabilistic latent semantic analysis for personalized web search
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we explore the benefits of latent variable modelling of clickthrough data in the domain of image retrieval. Clicks in image search logs are regarded as implicit relevance judgements that express both user intent and important relations between selected documents. We posit that clickthrough data contains hidden topics and can be used to infer a lower dimensional latent space that can be subsequently employed to improve various aspects of the retrieval system. We use a subset of a clickthrough corpus from the image search portal of a news agency to evaluate several popular latent variable models in terms of their ability to model topics underlying queries. We demonstrate that latent variable modelling reveals underlying structure in clickthrough data and our results show that computing document similarities in the latent space improves retrieval effectiveness compared to computing similarities in the original query space. These results are compared with baselines using visual and textual features. We show performance substantially better than the visual baseline, which indicates that content-based image retrieval systems that do not exploit query logs could improve recall and precision by taking this historical data into account.