Microsoft Technology Licensing, LLC

Shaunak Chatterjee

Shipeng Yu

Jinyun Yan

Ajith Muralidharan

Ankan Saha

Souvik Ghosh

Romer E Rosales

Kinjal Basu

Yiping Yuan

Ye Tu

Deepak Agarwal

Bee-Chung Chen

Rupesh Gupta

Birjodh S Tiwana

Yijie Wang

Romer E Rosales-Delmoral

Chun Lo

Aastha Jain

Hsiao-Ping Tseng

Hongche Liu

Joshua Daniel Hartman

Pannagadatta K Shivaswamy

Shubhanshu Nagar

Sandor Nyako

Myunghwan Kim

Rishi Jobanputra

Cameron Alexander Lee

Changji Shi

Divya Venugopalan

Shilpa Gupta

Ryan Oblak

Ying Han

Gaurav Chandalia

Qiang Wu

Wei W Wang

Yang Li

Liang Zhang

Yu Shi

Wei Guang Lu

Yang Yang

Mark Hull

Zheng Li

Weidong Zhang

Mitul Tiwari

Yuan Gao

Mohamed Gamal Mohamed Mahmoud

Ankit Gupta

Grace W Tang

Ravi Kiran Holur Vijay

Joel Daniel Young

Curtis Wang

Boyi Chen

David J Stein

Lokesh Pooranmal Bajaj

Timothy Paul Jurka

Parinkumar D Shah

Brad Christopher Ciraulo

Guanchao Wang

Yan Gao

Wei Zhuang

Pratik Daga

Wenxuan Gao

Karan Ashok Ahuja

Paul Taylor Ogilvie

Amit Chandak

Siva Visakan Sooriyan

Aastha Nigam

Sowmitra Thallapragada

Sammy Shreibati

Guangde Chen

Lingjie Weng

Swapnil Ghike

Matthew Hsing Hung Walker

Sarah Yan Xing

Jesse Jyh-Cherng Hsia

Hema Raghavan

Guanfeng Liang

Vivek Nelamangala

Vivian Mak Urata

Zhongen Tao

Paul Fletcher

Manoj Sivakumar

Zhiyuan Xu

Peng Du

Haiyang Liu

Wei Xue

Bonnie Barrilleaux

Befekadu Ayenew Ejigou

Ningfeng Liang

Wei Deng

Pingjie Xiao

Banu Muthukumar

Eric Brownrout

Viral Gupta

Michael Joshua Aft

Emilie De Longueau

Preetam Nandy

Miao Cheng

Varnit Agnihotri

Divya Gadde

In an example embodiment, a separate mimicry machine-learned model is trained for each of a plurality of different item types. Each of these models is trained to estimate an effect of mimicry for a user (i.e., a user whose user profile or other information is passed to the corresponding mimicry machine-learned model at prediction-time). The output of these models may be either used on its own to perform various actions, such as modifying a location of a user interface element of a user interface, or may be passed as input to an interaction machine-learned model that is trained to determine a likelihood of a user (i.e., a user whose user profile or other information is passed to the interaction machine-learned model at prediction-time) interacting with a particular item, such as a potential feed item.

Deep embedding learning models with mimicry effect

Methods, systems, and computer programs are presented for selecting notifications based on an affinity score between a content generator and a viewer of the content. One method includes capturing interactions of content generators with notifications, received by the content generators, associated with viewer responses to creator-generated content items. The method further includes training a machine-learning model based on the interactions, and detecting a first set of notifications, for a first content generator, associated with interactions of a set of viewers to first-content generator content. The ML model calculates an affinity score between the first content generator and each viewer, and the set of first notifications are ranked based on the affinity scores of the first content generator and the viewer associated with each notification. A set of second notifications is selected based on the ranked first notifications; and generating notifications are generated, for the first content generator, for the selected set of second notifications.

Leveraging affinity between content creator and viewer to improve creator retention

The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.

Framework for managing features across environments

A machine for improving content delivery generates a graph representing a personalized conversational flow for sequenced delivery of digital content. The graph includes nodes representing interactive dialogues between a machine and a user, and edges that connect the nodes. The machine causes display of a user interface including a prompt related to job-seeking guidance. The machine, based on a first action in response to the prompt, dynamically adjusts the graph, the dynamic adjusting including selecting a first node. The machine generates and causes display of a first incentive content item, and a first call-to-action content item. The machine, in response to a second action received in response to the first call-to action content item, dynamically selects an edge connecting the first node and a further node. The dynamic selecting of the edge results in display of a further incentive content item, and a further call-to-action content item.

Machine-learning-based application for improving digital content delivery

Methods, systems, and computer programs are presented for scheduling user notifications to maximize short-term and long-term benefits from sending the notifications. One method includes an operation for identifying features of a state used for reinforcement learning. The state is associated with an action to decide if a notification to a user is to be sent and a reward for sending the notification to the user. Further, the method includes capturing user responses to notifications sent to users to obtain training data and training a machine-learning (ML) algorithm with reinforcement learning based on the features and the training data to obtain an ML model. Additionally, the method includes receiving a request to send a notification to the user, and deciding, by the ML model, whether to send the notification based on a current state. The notification is sent to the user based on the decision.

User-notification scheduling

Techniques for nurturing content creation are provided. In one technique, a particular user is identified. Candidate entities are identified based on one or more attributes of the particular user. For each candidate entity, a feedback sensitivity measure of content creation of the candidate entity is determined. The feedback sensitivity measure is generated based on an amount of feedback, from other users, to content that the candidate entity has created. A score is then generated for the candidate entity based on the measure. A ranking of the candidate entities is determined based on the score of each candidate entity. A subset of the candidate entities is selected based on the ranking. The subset of the candidate entities is transmitted over a computer network to be presented on a computing device of the particular user.

Machine learning techniques to nurture content creation

Described herein is a contextual contact recommendation or suggestion service and system. The service, which, in some embodiments, is integrated with a social networking service and/or an instant messaging service, takes as input a first parameter that identifies a member of the social networking service, and a second parameter that defines a context (e.g., a web page that is being viewed by the member. The service, based in part on the context, computes a ranked list of members to populate a contextual contact list, thereby recommending or suggesting contacts, with whom the member might be interested in initiating, or continuing, a conversation, based on the context of the member's current web browsing session. Optionally, the service may take as input a third parameter, defining a use case, such that the recommendation algorithm can be customized by use case.

Techniques for presenting a contextual contact list

Methods, systems, and computer programs are presented for providing a user experience that facilitates navigation among different topics and articles on a social network. One method includes an operation for identifying a hierarchy of topics, each topic corresponding to a respective subject, where the hierarchy defines relationships between the topics. A first topic page for a first topic is presented in a user interface in the social network. The first topic page includes articles and first options for navigating to topic pages of topics related to the first topic. The method further includes detecting a selection of a first article. In response to detecting the selection, a first article page for the first article is presented in the user interface. The first article page includes details of the first article and second options for navigating to topic pages of topics related to the first article.

Social network navigation based on content relationships

Systems and methods for engagement mapping based on counterfactual experiments are provided. In example embodiments, a network system receives parameters for one or more counterfactual experiments or tests. Based on the parameters, the network system selects one or more users of a social network platform to subject to the test(s) and selects edges of a social network of each of the one or more users to block. The network system then filters out notifications and feed items from the selected edges of the one or more users. Behavior data of the one or more users based on the filtering out of the notifications and feed items is aggregated, whereby the behavior data indicates engagement of the one or more users on the social networking platform based on the filtering of the notifications and feed items. Recommendations are derived based on the aggregated behavior data and presented to the users.

Network liquidity to engagement mapping

Techniques for using a machine-learned model to personalize content item density. In one technique, an entity that is associated with a content request is identified. Multiple sets of content items are identified that includes content items of different types. A first position of a first slot is determined in a content item feed that comprises multiple slots. A second position of a previous content item is determined, in the content item feed, that is of a first type. A difference between the first position and the second position is determined. Based on the difference, a gap sensitivity value that is associated with the entity and is different than the difference is determined. Based on the gap sensitivity value, a content item from the multiple sets of content items is selected and inserted into the first slot. The content item feed is transmitted to a computing device to be presented thereon.

Using a machine-learned model to personalize content item density

A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Content Optimization Engine that determines a display probability for each content item in a set of content items. Each respective display probability corresponds to a given content item's probability of display in a specific content slot of a plurality of content slots in a social network feed of a target member account in a social network service. The Content Optimization Engine calculates a selection probability for each content item in an ordered set of the content items, based on each display probability and a set of interaction effects. The Content Optimization Engine causes display of the ordered set of content items in the target member account's social network feed based on satisfaction of the first and second targets.

Constrained multi-slot optimization for ranking recommendations

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

A plurality of potential feed objects and corresponding identifications of actors who performed a user interface action that caused a corresponding potential feed object to be generated are obtained. The plurality of potential feed objects and corresponding actor identifications are then fed into a machine learned feed object ranking model, with the machine learned feed object ranking model having been trained via a machine learning algorithm to calculate a score for each of the potential feed objects. The score is based on a combination of a likelihood that the user will perform an interaction, via the user interface, on the potential feed object, likelihood that the user's interaction will cause one or more downstream events by other users, and likelihood that a response from a viewer will cause the actor corresponding to the potential feed object to perform an additional user interface action to generate another potential feed object.

Feed actor optimization

A message spacing system evenly distributes the communication of one or more notifications to a computing device communicatively coupled with an online service. The message spacing system also instructs an application residing on the computing device to display a badge notification. The badge notification indicates a number of pending notifications awaiting review by a member of the online service. The badge notification may be overlaid an icon corresponding to an application that the member uses to access or interact with the online service. The badge notification may also be overlaid on an icon displayed on a webpage, where the icon represents a selectable topic that the member may select to interact with the online service. The notifications that the messaging spacing system may send include offline notifications and online notifications.

Message spacing system with badge notifications using online and offline notifications

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Sunnyvale, CA, United States of America

Shaunak Chatterjee