Abstract: A system that facilitates ranking search results returned by a search engine in response to receipt of a query is described herein. The system includes a receiver component that receives categorical metadata pertaining to an item and categorical metadata pertaining to the query and a computation component that computes at least one of a document feature pertaining to the item, a query feature pertaining to the query, or a document-query feature pertaining to the item and the query based at least in part upon one or more of the categorical metadata pertaining to the item or the categorical metadata pertaining to the query. The system also includes a ranker component that selectively places the item in a particular location in a sequence of items based at least in part upon the at least one of the document feature, the query feature, or the document-query feature.

Abstract: The hierarchical approach may start at the bottom of the hierarchy. As it moves up the hierarchy, knowledge from children and cousins is used to classify items at the parent. In addition, knowledge of improper classifications at a low level are raised to a higher level to create new rules to better identify mistaken classifications at a higher level. Once the top of the hierarchy is reached, a top down approach is used to further refine the classification of items.

Abstract: The invention applies a probabilistic approach to combining evidence regarding the correct classification of items. Training data and machine learning techniques are used to construct probabilistic dependency models that effectively utilize evidence. The evidence includes the outputs of one or more classifiers and optionally one or more reliability indicators. The reliability indicators are, in a broad sense, attributes of the items being classified. These attributes can include characteristics of an item, source of an item, and meta-level outputs of classifiers applied to the item. The resulting models include meta-classifiers, which combine evidence from two or more classifiers, and tuned classifiers, which use reliability indicators to inform the interpretation of classical classifier outputs. The invention also provides systems and methods for identifying new reliability indicators.