Abstract: Systems and methods according to embodiments leverage click data to predict a relevance judgment for a given query-content item pair. An initial training phase utilize a training set of query-content item pairs coupled with click data and relevance data (e.g., relevance judgments or labels) to train a model of the relationship between relevance and clicks. Accordingly, given an unlabeled query-content item pair as input to the model, a relevance judgment or label is provided. Theses relevance labels, in turn, may be used in conjunction with query-content item pairs with which they are associated to train a model to determine a content item relevance function. When a user provides a query to a given search engine, the search engine applies the content item relevance function to the query and content items in a responsive result set to provide a relevance ordered result set to the user.

Abstract: Systems and methods according to embodiments leverage click data to predict a relevance judgment for a given query-content item pair. An initial training phase utilize a training set of query-content item pairs coupled with click data and relevance data (e.g., relevance judgments or labels) to train a model of the relationship between relevance and clicks. Accordingly, given an unlabeled query-content item pair as input to the model, a relevance judgment or label is provided. Theses relevance labels, in turn, may be used in conjunction with query-content item pairs with which they are associated to train a model to determine a content item relevance function. When a user provides a query to a given search engine, the search engine applies the content item relevance function to the query and content items in a responsive result set to provide a relevance ordered result set to the user.