A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Regularizing ad hoc retrieval scores
Proceedings of the 14th ACM international conference on Information and knowledge management
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
ACM SIGIR Forum
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Smoothing clickthrough data for web search ranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Discovering missing click-through query language information for web search
Proceedings of the 20th ACM international conference on Information and knowledge management
Aggregated search interface preferences in multi-session search tasks
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Evaluating aggregated search using interleaving
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Modeling clicks beyond the first result page
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Clickthrough data is a critical feature for improving web search ranking. Recently, many search portals have provided aggregated search, which retrieves relevant information from various heterogeneous collections called verticals. In addition to the well-known problem of rank bias, clickthrough data recorded in the aggregated search environment suffers from severe sparseness problems due to the limited number of results presented for each vertical. This skew in clickthrough data, which we call rank cut, makes optimization of vertical searches more difficult. In this work, we focus on mitigating the negative effect of rank cut for aggregated vertical searches. We introduce a technique for smoothing click counts based on spectral graph analysis. Using real clickthrough data from a vertical recorded in an aggregated search environment, we show empirically that clickthrough data smoothed by this technique is effective for improving the vertical search