Abstract: The probability that an entity will purchase a product is predicted. This prediction involves receiving input data in the form of entries each of which represents an interest event. Through a series of iterations, a final matrix is generated from the input data entries by assigning an entity-product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event occurred from a prescribed date of interest. A supervised machine learning technique is employed to create a final prediction model for each product of interest using the final matrix as input. For each product, its prediction model is applied to the input data to estimate the probability that an entity will purchase the product within a future time period.

Abstract: Resume data identification implementations are described that identify anonymized resume corpus data pertaining to the same individual. In one implementation, identifying this data in the anonymized resume corpus involves segmenting the corpus into resume snippets and clustering the resume snippets into groups. Within each group the resume snippets potentially pertain to the same individual. In addition, one or more optional filtering operations can be performed to remove snippets from a group that are less likely to be associated with the same person. This filtering is especially useful when the resume corpus is large.

Abstract: Resume data identification implementations are described that identify anonymized resume corpus data pertaining to the same individual. In one implementation, identifying this data in the anonymized resume corpus involves segmenting the corpus into resume snippets and clustering the resume snippets into groups. Within each group the resume snippets potentially pertain to the same individual. In addition, one or more optional filtering operations can be performed to remove snippets from a group that are less likely to be associated with the same person. This filtering is especially useful when the resume corpus is large.

Abstract: The probability that an entity will purchase a product is predicted. This prediction involves receiving input data in the form of entries each of which represents an interest event. Through a series of iterations, a final matrix is generated from the input data entries by assigning an entity-product identifier pair associated with each interest event to a different location in the matrix, along with a time identifier indicative of how far back in time the interest event occurred from a prescribed date of interest. A supervised machine learning technique is employed to create a final prediction model for each product of interest using the final matrix as input. For each product, its prediction model is applied to the input data to estimate the probability that an entity will purchase the product within a future time period.