Abstract: A system can include a processor configured to: receive user session data from a network, identify user session data associated with a creative, and determine user interaction information associated with the creative. The processor may also be configured to determine one or more of a mean, a variance, and a median of a distribution of the user interaction information associated with the creative. Also, the processor can be configured to determine expected user engagement associated with the creative according to one or more of the user interaction information, the mean, the variance and the median. The processor can also be configured to: determine a probability that the expected user engagement will be higher than actual user engagement according to the expected user engagement and determine an expected price associated with the creative according to the probability that the expected user engagement will be higher than actual user engagement.

Abstract: Methods and systems are provided that may be utilized to extract hyper-local event information from one or more web pages.

Abstract: The relevance of an object, such as a document resulting from a query, may be determined automatically. A graphical model-based technique is applied to determine the relevance of the object. The graphical model may represent relationships between actual and observed labels for the object, based on features of the object. The graphical model may take into account an assumption of noisy training data by modeling the noise.

Abstract: Described is a technology in which documents associated with a query are ranked by a ranking model that depends on the query. When a query is processed, a ranking model for the query is selected/determined based upon nearest neighbors to the query in query feature space. In one aspect, the ranking model is trained online, based on a training set obtained from a number of nearest neighbors to the query. In an alternative aspect, ranking models are trained offline using training sets; the query is used to find a most similar training set based on nearest neighbors of the query, with the ranking model that corresponds to the most similar training set being selected for ranking. In another alternative aspect, the ranking models are trained offline, with the nearest neighbor to the query determined and used to select its associated ranking model.