Microsoft Technology Licensing, LLC

Google Inc.

Sumedha K Swamy

Xi Chelsea Chen

Moises Morgenstern Gali

Keqing Liang

Qing Duan

Hongjie Bai

Florian Raudies

Qianqi Shen

Luthfur R Chowdhury

Shireesh Agrawal

Methods, systems, and computer programs are presented for presenting return-on-investment (ROI) information, for budgeted services that resulted in a successful service delivery, on a user interface for setting the budget for a service request. One method includes an operation for identifying daily budgets for budgeted services that resulted in a successful service delivery (BSSSD). Each daily budget indicates an amount for spending in promotion of the BSSSD in an online service. The method further includes receiving a request, in a graphical user interface (GUI) of the online service, for posting a daily budget for a first budgeted service. Further, a performance value, associated with the daily budgets of the BSSSD that are similar to the first budgeted service, is selected. Further, the method includes causing presentation, by the one or more processors, of the performance value in the GUI.

Job-post budget recommendation based on performance

The disclosed embodiments provide a system for forecasting job applications. During operation, the system applies a first machine learning model to features that include a budget for a job to predict a number of impressions of the job. Next, the system applies a second machine learning model to additional features for the job to estimate an application rate for the job. The system then determines a distribution of applications to the job based on the number of impressions and the application rate. Finally, the system outputs one or more values from the distribution of applications to the job as guidance for setting the budget for the job.

Recommending a budget for an online job posting utilizing machine learning forecasting

A data processing system generates a mapping between entries in an index to determine an object represented by digital content of a network address. The system includes a network interface system that receives script data and a content title of digital content; a data retrieval interface that retrieves executable logic; a parsing engine that executes the executable logic to extract an identifier of an object; a search engine that searches an index to identify a first entry with indexed data representing a descriptor of the object for which the identifier is extracted; and a mapping engine that generates, in the index, a second entry with data representing the content title of the digital content and with data representing the identifier of the object that is referenced by the digital content accessible at the network address, and a mapping between the first entry and the second entry.

Object identifier index

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San Jose, CA, United States of America

Sumedha K Swamy