Snap Inc.

Netseer, Inc.

Neetseer, Inc.

Nima Khajehnouri

Jason Brewer

Yiwei Ma

Shuo Wu

Masoud Akhoondi

Rohan Monga

Riccardo Boscolo

Behnam Attaran Rezaei

David B Lue

Vwani P Roychowdhury

Rodrigo B Farnham

Zhuo Xu

John Cain Blackwood

John Mracek

Hadi Minooei

Benjamin C Steele

Qian You

Sanjiv Ghate

Albert Chern

Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.

Systems, devices, and methods for content selection

A visitation tracking system tracks user visitation to locations of interest to generate and report visualizations of the user visitation data. The visitation tracking system may be or include a group of one or more server machines configured to receive a user input specifying a location of interest, cause display of a presentation of a set of proximity borders within a region in response to the user input specifying the location of interest, wherein the region is based on the location of interest, receives a selection of a region to monitor (e.g., proximity borders, geo-fences), detects entries of client devices to a geo-fence represented by the region to monitor, and causes display of a notification in response to the detection of the entries.

System to track visitation metrics

Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Machine learning modeling using social graph signals

Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Concept-level user intent profile extraction and applications

A method for generating a conceptual association graph from structured content includes grouping content nodes into one or more topically biased clusters, the content nodes comprising structured digital content and unstructured digital content, the grouping based at least in part on the connectedness of each content node member to other content node members in the same cluster. The method also includes, responsive to the grouping, tagging the content nodes with one or more descriptive concepts. The method also includes, responsive to the tagging, establishing one or more associations between the one or more concepts, the one or more associations indicating a relevance of the one or more associations, the indicating based at least in part on patterns of co-occurrence of concepts in the tagged content nodes.

Generating a conceptual association graph from large-scale loosely-grouped content

Growing community of inventors

Los Angeles, CA, United States of America

Nima Khajehnouri