Google Inc.

Facebook, Inc.

Eightfold Ai Inc.

Meta Platforms, Inc.

Varun Kacholia

Ashutosh Garg

David P Stoutamire

Zhihui Chen

Sahil Prakash Thaker

Yuanxuan Wang

Hao Kai Zhang

Nitin Khandelwal

Xiaojun Liang

Maher Afif Saba

Erich James Owens

Nicola Muscettola

Mahmud Sami Tas

Deepa Diwakar

Brett Welch

Gang Liu

Fidji Nahema Simo

Li Zhang

Kun Zhang

Kedar Dhamdhere

Gheorghe Postelnicu

Hyung-Jin Kim

Jonathan Frankle

Yu He

David Vickrey

Guy Dassa

Aparna Chennapragada

Omer Bar-or

Richard Tucker

Holly Marie Ormseth

Henele Iitaka Adams

Srinath Sridhar

Patrick Hung

Khalid Bakry El-Arini

Murali Krishna Viswanathan

Minya Dai

Sanjeet Uday Hajarnis

Zhiyi Xie

Prasoon Mishra

Elad Gerson

Gaurav Shankar

Jonathan Eng

Sriya Santhanam

Ruoyu Roy Wang

A method and system for predicting a match between a candidate and a job position for an organization include generating an enriched talent profile associated with the candidate, the enriched talent profile comprising first characteristic values and second characteristic values, wherein the first characteristic values relate to characteristics verifiably possessed by the candidate, and second characteristic values are predicted values by executing a first neural network module, generating a calibrated job profile for the job position, the calibrated job profile comprising job requirements for the job position, and executing a second neural network module using the enriched talent profile and the calibrated job profile as inputs to calculate one or more hire-related prediction values for the candidate.

System, method, and computer program for automatically predicting the job candidates most likely to be hired and successful in a job

Systems, methods, and non-transitory computer readable media configured to determine a value associated with at least one signal indicative of objectionable material in a content item. The value associated with the at least one signal indicative of objectionable material can be compared with a threshold value associated with the at least one signal. A demotion value can be determined in response to satisfaction of the threshold value associated with the at least one signal.

Systems and methods for demotion of content items in a feed

Systems, methods, and non-transitory computer-readable media can provide a content feed through a display interface. The content feed can include at least a snippet of a first content item that is associated with a preview time threshold. A determination can be made that the snippet of the first content item has been positioned within a viewport region of the display interface for a duration of time that satisfies the preview time threshold. Data associated with the first content item can be obtained from a content distribution system. The data associated with the first content item can be stored.

Systems and methods for content presentation

A system and computer program enable a candidate to anonymously apply for a job position at an organization. In response to submitting a resume or other data for the purpose of applying anonymously for a job, the system generates an anonymous profile for the candidate and provides it to the organization to which the candidate is applying. The system excludes the candidate's name from the anonymous profile. In certain embodiments, the system also excludes one or more of the following: the candidate's address, data that is indicative of a candidate's race, age, or gender, and data that is not relevant for the job role for which the candidate is applying. After reviewing the anonymous profile, the organization has the option to reject the candidate or explore the candidate further. In response to the organization rejecting the candidate, the system notifies the candidate of the rejection without revealing the candidate's identity to the organization.

System, method, and computer program for enabling a candidate to anonymously apply for a job

The present disclosure describes a system, method, and computer program for removing or replacing information in candidate profiles that may influence bias. The system identifies data in candidate profiles that may influence bias with respect to one or more defined classes of bias, such as gender, race, or age. The identified data is removed or replaced with an abstracted/neutral form of the data. The system may also remove information that personally identifies a candidate. In certain embodiments, the system determines what data is relevant for the job for which a candidate is applying and, for data that is relevant, at what level it is relevant (i.e., at the base value or an abstracted level). In such embodiments, the system removes data from the candidate's profile that is not relevant for the job role and, where applicable, abstracts some of the remaining data to levels that are relevant for the job role.

System, method, and computer program for automatically removing data from candidate profiles that may influence bias

An intelligent system and method for analyzing documents and suggesting corrections based on diversity criteria include a processing device to analyze a job document, using a machine learning model, to identify a first expression representing a first qualification requirement favorable to a first class of applicants than a second class of applicants according to a diversity metric, responsive to identifying the first expression, determine, using a semantic relation map, a second expression representing a second qualification requirement that is less favorable to the first class of applicants when compared to the first expression, and responsive to determining that the second expression, present the second expression on the interface device as a suggested replacement to the first expression in the job document.

System, method, and computer program for using machine learning to calibrate job description based on diversity criteria

A method and system relating to determine a match between a candidate and a job position for an enterprise, the method and system including receiving an enriched talent profile associated with the candidate, the enriched talent profile comprising a talent profile of the candidate derived from a first data source of the enterprise, and one or more supplemental data items obtained from a second data source independent from the first data source, generating a calibrated job profile for the job position, the calibrated job profile comprising information, and executing a machine learning module using the enriched talent profile and the calibrated job profile as inputs to determine a match score between the candidate and the job position.

Systems and method for content presentation

An online system generates a feed of content items for a user subject to a limitation restricting the number of content items including video data ('video content items') included in the feed. If the user interacts with a video content item the feed, the online system selects candidate video content items based on characteristics of the video content item in the feed and characteristics of the user. The online system determines likelihoods of the user interacting with various candidate video content items and selects candidate video content items based on the determined likelihoods. To present the user with additional video content items, the online system generates an interface including the selected candidate video content items and presents the interface to the user. The interface may be presented in place of the feed or may be presented as within the feed and presents different video content items based on user interactions.

Presenting video content to online system users in response to user interactions with video content presented in a feed of content items

Offer listings can be classified as accessory offers or product offers using a classification operation performed on a corpus of offers. Data from the classification operation can be used to classify received queries as either product or accessory, and to classify results as products or accessories for purposes of presenting a relevant list of results to a user.

Distinguishing accessories from products for ranking search results

Systems, methods, and non-transitory computer readable media configured to detect access by a user to an original content item relating to a story. At least one of a comments based technique, a token based technique, and a tag based technique is performed on content items. Constraints are applied to identify at least one follow up content item from the content items relating to a development of the story.

Systems and methods for recommending follow up content

A system, a method, and various software tools enable a video hosting website to automatically identify posted video items that contain spam in the metadata associated with a respective video item. A spam detection tool for user-generated video items based on concept clustering is provided that facilitates the detection of spam in the metadata associated with a video item.

Spam detection for user-generated multimedia items based on concept clustering

The present disclosure relates to the identification of video content. In one aspect, a method includes generating a query based on bibliographic data. The method also includes obtaining a collection of resources responsive to the query, wherein one or more of the resources include text and video content. The method further includes calculating occurrence scores for the resources. A particular occurrence score for a particular resource is based at least in part on the bibliographic data matching text included in the particular resource and the text being associated with video content. The method further includes selecting one or more resources as including video content identified by the bibliographic data using the occurrence scores. The method further includes storing data associating the selected resources with the bibliographic data.

Matching video content to video bibliographic data

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Sunnyvale, CA, United States of America

Varun Kacholia