Microsoft Technology Licensing, LLC

Mohammad Raza

Sumit Gulwani

Thore Graepel

Euan Peter Garden

Chairy Chiu Ying Cheung

Daniel Galen Simmons

Ranvijay Kumar

Various technologies described herein pertain to automated data splitting using predictive program synthesis. Input-only examples for splitting an input column of an input data set can be received. The input-only examples can include example entries from the input column of the input data set to be split into multiple output columns without specification of how the example entries are to be split into multiple output columns. Further, a program for splitting the input column of the input data set into the multiple output columns can be synthesized based on the input-only examples. The program can be synthesized, given the input-only examples, in a domain-specific language (DSL) for splitting an entry into a tuple of fields utilizing a predictive program synthesis algorithm. Moreover, the program can be executed on the input data set to split the input column of the input data set into the multiple output columns.

Automatic splitting of a column into multiple columns

Various technologies described herein pertain to interactive data splitting. A program for splitting an input column of an input data set into multiple output columns can be synthesized based on input-only examples. The program can further be generated based on various user input; thus, the user input can guide the synthesis of the program. Moreover, the program can be executed on the input data set to split the input column of the input data set into the multiple output columns.

Interactive splitting of a column into multiple columns

Systems, methods, and computer-executable instructions for synthesizing a program for a task. A domain specific language that includes a disjunctive symbol and an input/output example for the task are received. A program for the task is synthesized based on the input/output example and the domain specific language. Sub-programs for an instance of the disjunctive symbol are synthesized, A set of feature calculators and target feature values are determined. The program for the task is returned. The program includes the sub-programs, the set of feature calculators and the target feature values.

Programming-by-example using disjunctive programs

Predicting user responses to items is useful in many application domains, such as personalized information retrieval and recommendation systems. In an embodiment a contacts service identifies contacts of a target user and predictions are elicited from the contacts about the target user's response to an item. In various examples, the predictions are combined taking into account weights of the contacts to produce a prediction of the target user's response. For example, the response may be one or more of: a numerical rating, a word or phrase describing the targets user's opinion of the item and a word or phrase stating a reason that the target user holds the opinion. In examples, accuracy of the predictions is calculated after observing the target user's actual response. The accuracy may be used to calculate and display scores and rankings of the contact's prediction abilities and to update the weights of the contacts.

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Mohammad Raza