Microsoft Technology Licensing, LLC

Romer E Rosales-Delmoral

Shaunak Chatterjee

Rupesh Gupta

Yu Shi

Jeffrey William Pasternack

Mitul Tiwari

Hsiao-Ping Tseng

Bing Zhao

Siyu You

Vita G Markman

Souvik Ghosh

Joshua Daniel Hartman

Yiping Yuan

Shubhanshu Nagar

Myunghwan Kim

Lingjie Weng

Cameron Alexander Lee

Baolei Li

Rishi Jobanputra

Ryan Oblak

Methods, systems, and computer programs are presented for estimating the language used in a user communication. One method includes an operation for utilizing counters to track use of languages by a user of an online service, the counters being updated based on interactions of the user in the online service. Further, the method includes operations for detecting a text entered by the user and obtaining, by a language classifier, an initial prediction having probabilities for the languages that the text is in the language. A language distribution prediction is calculated based on the initial prediction and the user counters, where the language distribution prediction comprises a probability, for each language, that the text is in the language. Further, the method includes operations for selecting a language used in the text based on the language distribution prediction and causing presentation on a display of a message in the selected language.

Language profiling service

A method can include determining a first probability that a first member of members of a website will visit the website within a specified time window if the first member is provided an intervention at a specified time, determining a second probability that the first member will visit the website within the specified time window without being provided the intervention, determining a difference between the first and second probability, and in response to determining the difference is greater than a first specified threshold, providing the intervention at the specified time.

Predicting site visit based on intervention

This disclosure provides for systems and methods that generate personalized electronic messages for members of a networked communication service. The personalized electronic messages are generated according to commonalities between member profiles. In one approach, an electronic message template is referenced based on the commonalities. The electronic message template is populated with member profile attribute values selected from the member profiles. In another approach, a long-short term memory (LSTM) recurrent neural network (RNN) is used to generate the electronic messages. Under this approach, a sequence-to-sequence model is trained using previous electronic messages labeled with one or more member profile attributes and/or member profile attribute values. When provided with one or more member profile attribute values associated with matching member profiles, the LSTM RNN outputs a relevant and appropriate electronic message designed to create an interest in the recipient of the electronic message.

Writing personalized electronic messages using template-based and machine-learning approaches

A method of optimizing online advertising campaign allocations is disclosed. It is determined that an auction for a set of advertising slots has been triggered. It is identified that the advertising campaigns are configured to bid on the set of advertising slots. A ranking score for each of the advertising campaigns is determined. The ranking scores are adjusted for each cost-per-click advertising campaign of the set of advertising campaigns by an adjustment factor specific to a context of the auction. The set of advertising slots is allocated to the winners of the auction. The winners of the auction are communicated for integration into a content page.

Optimizing online advertising campaign allocations

This disclosure relates to systems and methods for managing multiple messages. In one example, a method includes determining a message transmission frequency threshold for a member of an online social networking service using responses from the member; receiving a message that is to be transmitted to the member; storing the message, without transmitting the message to the member, in a digest of messages for the member; and transmitting the digest to the member in response to a send score for the digest exceeding a send score threshold, the send score calculated using the number of messages in the digest.

Managing multiple messages

The disclosed embodiments provide a system for facilitating interaction within a social network. During operation, the system obtains a set of attributes of a social network of a first member and a set of historic interactions in the social network. Next, the system analyzes the attributes and the historic interactions to predict an effect of a potential interaction between the first member and a second member of the social network on subsequent interactions in the social network. The system then uses the predicted effect to generate output for modulating the subsequent interactions in the social network.

Characterizing and managing social network interactions

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Burlingame, CA, United States of America

Romer E Rosales-Delmoral