An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Faceted exploration of image search results
Proceedings of the 19th international conference on World wide web
Web searching with entity mining at query time
IRFC'12 Proceedings of the 5th conference on Multidisciplinary Information Retrieval
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The research described in this paper forms the backbone of a service that enables the faceted search experience of the Yahoo! search engine. We introduce an approach for a machine learned ranking of entity facets based on user click feedback and features extracted from three different ranking sources. The objective of the learned model is to predict the click-through rate on an entity facet. In an empirical evaluation we compare the performance of gradient boosted decision trees (GBDT) against a linear combination of features on two different click feedback models using the raw click-through rate (CTR), and click over expected clicks (COEC). The results show a significant improvement in retrieval performance, in terms of discounted cumulated gain, when ranking entity facets with GBDT trained on the COEC model. Most notably this is true when evaluated against the CTR test set.