Oath Inc.

Maplebear Inc.

David Pal

Zohar Karnin

Sharath Rao Karikurve

Peng Qi

Satyen Chandrakant Kale

Jyh-Shin Shue

Jeffrey Bernard Arnold

Yuval Peduel

Jonathan Gu

Yongxin Xi

Rob Donnelly

Sumit Garg

Ilambharathi Kanniah

Bill Lundberg

An online concierge system includes sponsored content items in an interface including different slots for displaying content items. A sponsored content item may be displayed in a single slot or in multiple adjacent slots. The online concierge system determines a content score for various sponsored content items indicating a likelihood of a user interacting with a sponsored content item and a position bias for slots in the interface indicating a likelihood of the user interacting with a slot independent of content in the slot. Position biases are different dependent on a number of slots in which a content item is displayed. The online concierge system generates a graph identifying potential placements of sponsored content items in slots by selecting content items in an order according to their content scores. Sponsored content items are positioned in slots according to a path through the graph that has the highest overall expected value.

Method, computer program product, and system for accounting for variable dimensions of content items when positioning content items in a user interface having slots for displaying content items

One or more computing devices, systems, and/or methods for message classification are provided. For example, a set of messages is clustered into a set of clusters. A cluster comprises messages with similar features (e.g., similar subject lines, message body content, sender information, recipient information, structure, user action such as reading or deleting, spam vote information, etc.). Cluster features are computed for the clusters based upon features of messages within such clusters. A first table, comprising cluster entries corresponding cluster features of clusters, and a second table, comprising message entries corresponding to clusters to which messages are assigned, are created. Message features of a message are created, using the first table and second table, based upon features of the message and cluster features of clusters to which the message is assigned. A message classifier is used to classify the message (e.g., spam, safe, a threat, etc.) based upon the message features.

Message classification

Growing community of inventors

New York, NY, United States of America

David Pal