Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

Abstract: A record management system retrieves relevance information through an information retrieval model that models relevance between users, queries, and records based on user interaction data with records. Relevance information between different elements of the record management system are determined through a set of learned transformations in the information retrieval model. The record management system can quickly retrieve relevance information between different elements of the record management system given the set of learned transformations in the information retrieval model, without the need to construct separate systems for different types of relevance information. Moreover, even without access to contents of records, the record management system can determine relevant records for a given query based on user interaction data and the determined relationships between users, queries, and records learned through the information retrieval model.

Abstract: An online system ranks test cases run in connection with check-in of sets of software files in a software repository. The online system ranks the test cases higher if they are more likely to fail as a result of defects in the set of files being checked in. Accordingly, the online system informs software developers of potential defects in the files being checked in early without having to run the complete suite of test cases. The online system determines a vector representation of the files and test cases based on a neural network. The online system determines an aggregate vector representation of the set of files. The online system determines a measure of similarity between the test cases and the aggregate vector representation of the set of files. The online system ranks the test cases based on the measures of similarity of the test cases.

Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

Abstract: An online system ranks test cases run in connection with check-in of sets of software files in a software repository. The online system ranks the test cases higher if they are more likely to fail as a result of defects in the set of files being checked in. Accordingly, the online system informs software developers of potential defects in the files being checked in early without having to run the complete suite of test cases. The online system determines a vector representation of the files and test cases based on a neural network. The online system determines an aggregate vector representation of the set of files. The online system determines a measure of similarity between the test cases and the aggregate vector representation of the set of files. The online system ranks the test cases based on the measures of similarity of the test cases.

Abstract: An online system stores objects representing potential transactions of an enterprise. The online system uses machine learning techniques to predict likelihood of success for a potential transaction object. The online system stores historical data describing activities associated with potential transaction objects and uses the stored data as training dataset for a predictor model. The online system extracts features describing potential transaction objects and provides these as input to the predictor model for predicting the likelihood of success of a given potential transaction. The online system may use predictions of likelihood of success of potential transactions to identify a set of potential transactions that should be acted upon to maximize the benefit the enterprise within a time interval, for example, by the end of the current month.

Abstract: A record management system retrieves relevance information through an information retrieval model that models relevance between users, queries, and records based on user interaction data with records. Relevance information between different elements of the record management system are determined through a set of learned transformations in the information retrieval model. The record management system can quickly retrieve relevance information between different elements of the record management system given the set of learned transformations in the information retrieval model, without the need to construct separate systems for different types of relevance information. Moreover, even without access to contents of records, the record management system can determine relevant records for a given query based on user interaction data and the determined relationships between users, queries, and records learned through the information retrieval model.

Abstract: A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.