Apple Inc.

Laserlike, Inc.

Vishnu Priya Natchu

Anand Shukla

Kushal Tayal

Srinivasan Venkatachary

Steven D Baker

Derek Kisman

Techniques for providing an enhanced search to generate a feed based on a user's interests are disclosed. In some embodiments, a system/process/computer program product for providing an enhanced search to generate a feed based on a user's interests includes receiving a plurality of interests associated with a user, searching online content including one or more websites (e.g., news or other content websites, social networking sites, and/or other online content) based on the plurality of interests associated with the user, receiving a plurality of web documents (e.g., links to websites, social networking sites, and other online content) based on the search for online content, ranking the plurality of web documents based on a document score and a user signal, and generating a content feed that includes at least a subset of the plurality of web documents based on the ranking.

Enhanced search to generate a feed based on a user's interests

An embedding associated with a query comprising one or more query terms is determined to be not stored in memory. An embedding is generated for the one or more query terms. One or more web documents that are similar to the generated embedding are determined. One or more content cards associated with the one or more web documents that are determined to be similar to the generated embedding are provided in a content feed.

Golden embeddings

Techniques for generating interest embedding vectors are disclosed. In some embodiments, a system/process/computer program product for generating interest embedding vectors includes aggregating a plurality of web documents associated with one or more entities, wherein the web documents are retrieved from a plurality of online content sources including one or more websites; selecting a plurality of tokens based on processing of the plurality of web documents; and generating embeddings of the selected tokens in an embedding space.

Interest embedding vectors

A set of documents associated with a query comprising one or more query terms is determined. A subset of the documents is sampled to identify a corresponding query constraint pattern associated with each document included in the subset of the documents. An entry of an inverted index is generated based on the corresponding query constraint pattern associated with each document included in the subset of document.

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Mountain View, CA, United States of America

Vishnu Priya Natchu