An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
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
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Query-sets: using implicit feedback and query patterns to organize web documents
Proceedings of the 17th international conference on World Wide Web
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Learning latent semantic relations from clickthrough data for query suggestion
Proceedings of the 17th ACM conference on Information and knowledge management
Automatic video tagging using content redundancy
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Learning to re-rank: query-dependent image re-ranking using click data
Proceedings of the 20th international conference on World wide web
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Video Annotation Through Search and Graph Reinforcement Mining
IEEE Transactions on Multimedia
Image ranking based on user browsing behavior
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Image search by graph-based label propagation with image representation from DNN
Proceedings of the 21st ACM international conference on Multimedia
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The problem of tagging is mostly considered from the perspectives of machine learning and data-driven philosophy. A fundamental issue that underlies the success of these approaches is the visual similarity, ranging from the nearest neighbor search to manifold learning, to identify similar instances of an example for tag completion. The need to searching for millions of visual examples in high-dimensional feature space, however, makes the task computationally expensive. Moreover, the results can suffer from robustness problem, when the underlying data, such as online videos, are rich of semantics and the similarity is difficult to be learnt from low-level features. This paper studies the exploration of user searching behavior through click-through data, which is largely available and freely accessible by search engines, for learning video relationship and applying the relationship for economic way of annotating online videos. We demonstrated that, by a simple approach using co-click statistics, promising results were obtained in contrast to feature-based similarity measurement. Furthermore, considering the long tail effect that few videos dominate most clicks, a new method based on~polynomial~semantic indexing is proposed to learn a latent space~for alleviating the sparsity problem of click-through data. The proposed approaches are then applied for three major tasks in tagging: tag assignment, ranking, and enrichment. On~a bipartite graph constructed from click-through data with~over 15 million queries and 20 million video URL clicks,~we showed that annotation can be performed for free with competitive performance and minimum computing resource, representing a new and promising paradigm for video tagging in addition to machine learning and data-driven methodologies.