Pinterest, Inc.

Microsoft Technology Licensing, LLC

Leland Stanford Junior University

Bo Zhao

John William Gupta Egan

Samuel Seth Weisfeld-Filson

Krishnaram Kenthapadi

Burkay Birant Orten

Koichiro Narita

Charles Joseph Rosenberg

Jurij Leskovec

Chantat Eksombatchai

Aditya Pal

Kaushik Rangadurai

Yitong Zhou

Shanhui Fan

Stuart MacDonald Ambler

Ariel Fuxman

Meir Orenstein

Ryan Wade Sandler

Parul Jain

David J Stein

Fedor Vladimirovich Borisyuk

Sid Assawaworrarit

Jacob Bollinger

David Hardtke

Parthiban Santhanam

Hongwei Li

Systems and methods for generating user notifications to a set of users of a social networking service is presented. For each user of a set of users of the social networking service, one or more machine learning models selects an optimal notification channel, an optimal notification template, and optimal personalization content for configurable elements of a selected notification template. Each of these determinations/selections is made according to and based on a likelihood of increased user engagement with the social networking service. Upon determining the notification channel, notification template, and personalizations to the template, the notification is generated and sent to the corresponding user.

Optimal notification

Systems and methods for recommending content to an online service subscriber are presented. For each subscriber, content items that were the subject of the subscriber's prior interactions are projected, via associated embedding vectors, into a content item embedding space. The content items, via their projections into the content item embedding space, are clustered to form a plurality of interest clusters for the subscriber. A representative embedding vector is determined for each interest cluster, and a plurality of these embedding vectors are stored as the representative embedding vectors for the subscriber. The online service, in response to a request for recommended content for a subscriber, selects a first representative embedding vector associated with the subscriber and identifies a new content item from a corpus of content items according to a similarity measure between the first representative embedding vector and an embedding vector associated with the new content item.

Recommending content to subscribers

Described are systems and methods for generating recommendation campaigns that optimize for both a desired short-term user behavior and a desired long-term user behavior. In comparison to existing techniques that focus on targeting advertisements or recommendations to specific individuals with a single goal of receiving an interaction with the advertisement from that individual (i.e., a desired short-term behavior), the disclosed implementations consider the long-term user behavior, such as increased visits to a website during a long-term rage, and generate a recommendation campaign that also optimizes for that desired long-term user behavior.

Recommendation campaigns based on predicted short-term user behavior and predicted long-term user behavior

In certain examples, methods and semiconductor structures are directed to an apparatus including a photon emitter such as an LED which operates over an emission wavelength range and a photo-voltaic device arranged relative to the photon emitter to provide index-matched optical coupling between the photo-voltaic device and the photon emitter for an emission wavelength range of the photon emitter.

Apparatuses and methods involving DC voltage conversion using photonic transformers

Systems and methods for identifying a selected set of users of a social networking service which, upon issuing a notice regarding a re-post activity of an item of content, will likely result in heightened user interaction with the service. Upon receiving a notice of a re-post action by a first user, the social networking service identifies a first set of users of the service that have posted that item of content. Scores are associated with the users of the first set of users, the scores indicating a likelihood of a user that, if notified of the re-post action of the first user, will result in heightened user interaction with the social networking service. A subset of users of the first set of users are identified according to their associated scores and a notice of the re-post action by the first user is sent to the users of this subset of users.

Optimal selection of notice recipients

Methods, systems, and computer programs are presented for searching job postings for a member of a social network based on transitions from educational institutions to companies. A method includes determining educational-company transition scores indicating a transition probability from educational institution to company. The method identifies jobs based on a search performed for a first member, with a profile including one or more educational institutions, each job associated with a respective company. A server determines a member-company transition score based on the educational-company transition scores of the educational institutions in the profile. For each job, a job affinity score is determined based on data of the job and the profile of the first member. The server ranks the jobs based on the member-company transition score of the company of the job and the job affinity score. Some of the ranked jobs are presented to the first member based on the ranking.

Job search based on member transitions from educational institution to company

Various techniques for identifying a target worker group are described herein. In one example, a method includes detecting a response to a task from each worker in a group of workers and detecting a set of characteristics that correspond to each worker, wherein each characteristic comprises at least one attribute. The method can also include detecting a first attribute that corresponds to workers that provide responses with an accuracy above a threshold value. Furthermore, the method can include identifying the target worker group, the target worker group comprising the workers corresponding to the detected first attribute. The method may also include sending an additional task to the target worker group.

Worker group identification

Methods, systems, and computer programs are presented for ranking and presenting companies that are experiencing high growth in positions fulfillable by a user in response to a search for the user. A method includes identifying, on a social network, jobs that are available for presentation to the user and offered by one or more companies. The method further includes identifying proxy users who have similar skills as the user. For each company, a server determines a position growth score based on the number of proxy users who have transitioned to or from the company within a predetermined amount of time. The server additionally ranks the jobs within a high-growth companies group for the user based on the position growth score for each job. The jobs are then presented to the user on a user interface according to the job ranking.

Ranking job offerings based on growth potential within a company

Methods and systems for generating queries from a behavioral index model are described. According to various embodiments, a system receives a query input including one or more search terms from a client device associated with a member profile. The system generates a query from the one or more search terms and one or more profile terms. The system causes one or more query machines to search one or more inverted indices using the query and receives one or more result of documents distributed across the inverted indices. The system generates an ordered list of the one or more result sets and causes presentation of the ordered list at a client device associated with the member profile.

Machine learned query generation on inverted indices

This disclosure is directed to determining various economic graph indices and, in particular, to systems and methods that leverage a graph analytic engine and framework to determine values assigned to graph nodes extracted from one or more member profiles, and visualizing said values to correlate skills, geographies, and industries. The disclosed embodiments include a client-server architecture where a social networking server has access to a social graph of its social networking members. The social networking server includes various modules and engines that import the member profiles and then extracts certain defined attributes from the member profiles, such as employer (e.g., current employer and/or past employers), identified skills, educational institutions attended, and other such defined attributes. Using these attributes as nodes, the social networking server constructs a graph using various graph processing techniques. The resulting graph is then used to correlate and rank the various attributes that define the graph.

Constructing graphs from attributes of member profiles of a social networking service

In an example, a granularity of company similarity is determined, wherein the granularity of company similarity is a level at which social networking data should be filtered. A weighted graph of companies is constructed at the granularity of company similarity, wherein each node in the weighted graph is a company and a directed edge exists in the weighted graph between a first node and a second node if the social networking data, at the granularity of company similarity, indicates that a transition occurred wherein a member who held a position at a company corresponding to the first node transitioned to a position at a company corresponding to the second node, wherein each directed edge contains a weight indicating a strength of relationship between nodes. The weighted graph of locations is traversed from a node corresponding to the target company in order to identify companies similar to the target company.

Growing community of inventors

Redwood City, CA, United States of America

Bo Zhao