Microsoft Technology Licensing, LLC

International Business Machines Corporation

Apple Inc.

Yahoo! Inc.

Qi He

Jaewon Yang

Feng Guo

Baoxu Shi

William Scott Spangler

Gencer Cili

Bin He

Karthik Anantharaman

Xiangpeng Jing

Fei Chen

Xiao Yan

Peide Zhong

Mikhail Obukhov

Vijay Venkataraman

Krisztian Kiss

Devrim Varoglu

Alosious Pradeep Prabhakar

Sridhar Prakasam

Rohit R Matolia

Dan Shacham

Nirlesh Koshta

Nitin Kuppelur

Uri Merhav

Sumit Bhatia

Kavya B Ravikumar

Shashanka Totadamane Ramappa

Pradeep Pangi

W Scott Spangler

Peiji Chen

Datong Chen

Angela Jiang

Rajkumar Krishnaperumal

Lin Zhu

Lester K Chan

Madhusudhan Cheripally

Ying Chen

Deepak Agarwal

Jayavel Shanmugasundaram

Erik Nathan Vee

Bee-Chung Chen

Noshir Cavas Wadia

Tomer Cohen

Boyi Chen

Pannagadatta K Shivaswamy

Marcus Aurelius Tylutki

Guy Lebanon

Mustafa Emre Kazdagli

Sivaramachandran Ganesan

Jacob Bollinger

How Jing

Wenjia Ma

Joey Bai

Shan Li

Long Ji Lin

Xin Jin

Lester Chan

Minji Yoon

Sufeng Niu

Sairam T S Gutta

Konstantin Salomatin

Shiqi Wu

Farzard Eskafi

Angie Zhu

Wei Kang

Weizhi Meng

Feishe Chen

Techniques for implementing a performance-adaptive sampling strategy towards fast and accurate graph neural networks are provided. In one technique, a graph that comprises multiple nodes and edges connecting the nodes is stored. An embedding for each node is initialized, as well as a sampling policy for sampling neighbors of nodes. One or more machine learning techniques are used to train a graph neural network and learn embeddings for the nodes. Using the one or more machine learning techniques comprises, for each node: (1) selecting, based on the sampling policy, a set of neighbors of the node; (2) based on the graph neural network and embeddings for the node and the set of neighbors, computing a performance loss; and (3) based on a gradient of the performance loss, modifying the sampling policy.

Performance-adaptive sampling strategy towards fast and accurate graph neural networks

Apparatuses, systems, and methods for SoR list management and PLMN selection based on an SoR list. A UE may receive an SoR container which may include a list of OPLMNs. The UE may store a prior list of OPLMNs stored on a USIM of the UE on a memory of the UE. The UE replace the prior list of OPLMNs stored on the USIM with the list of OPLMNs included in the SoR container. The UE may consider the prior list of OPLMNs as a lower priority list as compared to the list of OPLMNs stored on the USIM. Additionally, in response to determining to move from a current PLMN, the UE may scan for a new PLMN based on the list of OPLMNs stored on the USIM.

Steering of roaming PLMN list update and management

A user equipment (UE) is configured to prioritize a plurality of Public Land Mobile Networks (PLMNs) from an aggregate roaming priority public land mobile network (RPPLMN) list containing the plurality of PLMNs into a plurality of prioritized PLMNs and connect to a selected PLMN from the plurality of prioritized PLMNs based on a priority of the selected PLMN. The UE is further configured to determine if an application processor (AP) assisted location service is available, wherein the AP-assisted service is configured to identify a plurality of deployed PLMNs and a plurality of frequencies used by the plurality of deployed PLMNs at a current geographical location of the UE and when the AP) assisted location service is available, determining if a registered PLMN (RPLMN) is valid in a current mobile country code (MCC).

Reduced power roaming by an accessory device

In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.

Machine learned model framework for screening question generation

Techniques for scoring data items using a machine-learned model are provided. In one technique, multiple skills are identifying based on a job posting. Multiple attribute values of the job posting are identified. For each identified skill, multiple probabilities are identified, each probability corresponding to a different attribute value of the identified attribute values. The probabilities are input into a machine-learned model to generate multiple scores. Multiple skills of a candidate user are identified. An affinity score between the job posting and the candidate user is generated based the scores and the skills of the candidate user.

Generating a machine-learned model for scoring skills based on feedback from job posters

Apparatuses, computer readable medium, and methods are disclosed for verifying skills of members of an online connection network. The apparatus, computer readable medium, and methods may include a method including responding to a first member of the online connection network indicating a skill possessed by the first member by selecting a skill verification user interface (UI) to present to a second member of the online connection network where the first member and the second member are connected via the online connection network. The method may further include presenting the skill verification UI to the second member, where the skill verification UI presents an indication of the first member, an indication of the skill, and a query regarding a competence level of the skill possessed by the first member. The method may further include receiving a response to the query and determining a skill validation value of the skill for the first member based on the response and a machine learning model.

Skill validation

In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.

Multi-task learning framework for multi-context machine learning

Techniques are provided for using machine learning techniques to analyze textual content. In one technique, a potential item is identified within a document. An analysis of the potential item is performed at multiple levels of granularity that includes two or more of a sentence level, a segment level, or a document level. The analysis produces multiple outputs, one for each level of granularity in the multiple levels of granularity. The outputs are input into a machine-learned model to generate a score for the potential item. Based on the score, the potential item is presented on a computing device. In response to user selection of the potential item, an association between the potential item and the document is created. The association may be used later to identify a set of users to which the document (or data thereof) is to be presented.

Machine learning techniques for analyzing textual content

During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.

Semantic matching and retrieval of standardized entities

Techniques for selecting candidates using a personalized model are disclosed herein. In some embodiments, a computer system, for each candidate of a plurality of candidates, generating a corresponding confidence score for a combination of the candidate, a particular viewer, and a particular attribute based on a scoring model, with the corresponding confidence score being configured to indicate a likelihood that the particular viewer will select the corresponding candidate as a preference with respect to the particular attribute. The computer system then selects a subset of the plurality of candidates based on the corresponding confidence scores of the candidates in the subset, and causes the subset of candidates to be displayed on a computing device of the viewer along with a prompting for the viewer to select one of the selected subset of candidates as the preference with respect to the particular attribute.

Candidate selection using personalized relevance modeling system

A system and method of adjusting an affinity score between an entity pair in a social network is disclosed. The method may include determining, with a processor, whether a first member of the entity pair is a heavy user member. The method further includes if the first member is the heavy user member, determining, with the processor, an affinity adjustment factor between the first member and the second member, and adjusting, with the processor, the affinity score between the first member and the second member of the entity pair in accordance with the adjustment factor to determine an adjusted affinity score. The method may include determining, with the processor, whether a number of interactions on content items indicates that the first member is the heavy user member. The second member is associated with a content item that is being considered for display to the first member.

Adjusting heavy users' affinity for heavy user entity-pairs in a social network

A machine may be configured to generate a digital content feed based on at least explicit feedback from a member of a Social Networking Service (SNS). For example, the machine generates explicit input data describing digital content preferences of a member of the SNS based on a communication including explicit feedback data. The communication is received from a client device associated with the member. The machine accesses feature data pertaining to one or more items of digital content determined to be relevant to the member. The feature data describes one or more characteristics associated with the one or more items. The machine generates a feed of items of digital content for the member based on the explicit input data and the feature data. The machine causes a presentation of the feed of the items of digital content in a user interface of the client device associated with the user.

Feed modeling incorporating explicit feedback

An online social networking system extracts terms from an unstructured job title record. The system searches a job role taxonomy database with the extracted terms to identify job roles. For each job role identified, the system extracts a plurality of additional terms appearing in the unstructured job title record. For each additional term, the system maps the additional term to a standardized modifier, thereby identifying a job seniority modifier, a job specialty modifier, a job accreditation modifier, and a job status modifier for each additional term. The system creates a multi-dimensional standardized job title for the member profile or job posting by writing the job role, the job seniority modifier, the job specialty modifier, the job accreditation modifier, and the job status modifier to a standardization record in a standardization database.

Multi-dimensional job title logical models for social network members

An online social networking system receives an unstructured job title record from a profile of a member or a job posting. The system extracts a raw job title from the unstructured job title record, and extracts a first seniority level from the raw job title. The first seniority level is a seniority modifier associated with the raw job title. The system determines a second seniority level. The second seniority level is a company seniority within the company associated with the unstructured job title record. The system determines a third seniority level. The third seniority level is a seniority score for the member or the job posting. The system compares the seniority score with a second seniority score, and communicates with the member, or transmits the job posting to the member, based on the comparison of the seniority score and the second seniority score.

Deriving multi-level seniority of social network members

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San Jose, CA, United States of America

Qi He