Microsoft Technology Licensing, LLC

Birjodh S Tiwana

Shaunak Chatterjee

Sarah Yan Xing

Jinyun Yan

Arpit Dhariwal

Ian Ackerman

Madhulekha Arunmozhi

Jesse Jyh-Cherng Hsia

Haiyang Liu

Gaurav Chandalia

Marcos Kawazoe Aguilera

Jeffrey William Pasternack

Hitesh Ballani

Mark Hull

Yuan Gao

Bing Zhao

Kinjal Basu

Mahesh Balakrishnan

Christopher Szeto

Ali Sadiq Mohamed

Nandeesh Channabasappa Rajashekar

Nimesh Madhavan Chakravarthi

Siva Visakan Sooriyan

Sammy Shreibati

Adam Leon

Francisco José Claude Faust

Vivian Mak Urata

Adam Robert Peck

Qianru Zhang

Namit Sikka

Kurt Mcculloch

Michael Joshua Aft

Siddharth Dangi

Nisheedh Raveendran

Described herein is a technique for surfacing content for users of a connection network application. The technique involves performing a first query to fetch recently-impressed items viewed by a user of the connection network application and, concurrently with the first query, performing a second query of search nodes to generate a set of search node results. The technique allows for filtering the recently-impressed items from the set of search node results to generate a candidate set and applying a freshness factor to the candidate set. Updates are provided to a user display, based on results of applying the freshness factor to the candidate set.

Impression discounting for followfeed

Described herein is a technique to facilitate filtering during candidate retrieval stage performed by an information retrieval system that utilizes embedding models. An aNN indexing structure is created for each end-user, and in some instances, each activity type. This allows a single request for candidate content items to invoke a single process to obtain content items that satisfy the filtering criteria (e.g., in this case, the in-network requirement) from the separate per-end-user indexes.

Embedding-based retrieval techniques for feeds

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

Techniques for varying content item density are provided. A first minimum gap value is stored that dictates how close two content items of a first type may appear in a content item feed that contains content items of multiple types that includes the first type and a second type. The first minimum gap value is used to place content items in a first set of content item feeds. For each content item feed of the first set of content item feeds, performance data that indicates how well content items of the first type perform in the content item feed is generated. Based on the performance data and the first minimum gap value, a second minimum gap value that is different than the first minimum gap value is generated. The second minimum gap value is used to place content items in a second plurality of content item feeds.

Feedback based controller for varying content item density

Described herein is a technique to facilitate the sharing of a content item presented in a content feed of a social networking service. Upon detecting that a member has selected an option to share a content item, a content sharing interface is presented. The content sharing interface includes options share the content item publically via a content feed, and privately via a messaging service. The content sharing interface provides a ranked list of recommended recipients, where the recommended recipients in the list are selected and ordered based on several factors, including factors relating to the relationship between the sharing member and the recommended recipients, as well as factors relating to the subject matter of the content item and the likelihood that a recommended recipient would be interested in the content item.

Sharing content to multiple public and private targets in a social network

Disclosed are systems, methods, and non-transitory computer-readable media for generating recommended responses based on historical data. A messaging system receives a message as part of a communication session between a first client device and a second client device. The message originated from the first client device. The messaging system determines, using the message as input in a statistical model, a set of candidate responses for replying to the message. The statistical model was generated based on historical message data transmitted as part of previous communication sessions between a plurality of client devices. The set of candidate responses is a subset of a set of available candidate responses. The messaging system determines, based on a set of candidate selection rules, a subset of the candidate responses yielding a set of recommended responses to the message, and causes the set of recommended responses to be presented on the second client device.

Generating recommended responses based on historical message data

Techniques for automatically merging multiple content item queues are provided. In one technique, a first set of content items of a first type is identified. A second set of content items of a second type that is different than first type is identified. The first set of content items and the second set of content items are merged in a content item feed. Such merging involves, for a particular slot in the content item feed: determining a previous slot that contains a first content item from the first set; determining a number of slots between the previous slot and the particular slot; based on the number of slots, generating a score for a second content item from the second set; and based on the score, determining whether to insert, into the particular slot, the second content item or a third content item from the first set of content items.

Automatically merging multiple content item queues

A cloud statistics server generates statistics for a cloud service based on an identified data item and an identified operation. The cloud service may include various computing nodes and storage nodes. The cloud statistics may include expected completion times for the identified operation and the identified data item with respect to each of the computing nodes. A computing node may be selected to execute the identified operation based on the expected completion times. The generated statistics may be generated by the cloud statistics server using a network topology associated with the data item that is based on the latencies or expected transfer times between the various storage nodes and computing nodes, and a replication strategy used by the cloud service. The topology may be implemented as a directed graph with edge weights corresponding to expected transfer times between each node.

Selecting computing nodes in cloud service using replication topologies

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Dublin, CA, United States of America

Birjodh S Tiwana