Google Inc.

Anandsudhakar Kesari

Zoltan Gyongyi

Juthika Kiran Dabholkar

Darryl DeWeese

Qun Cao

Albert Segars

Yiyuan Xia

Vincent Zammit

Jay Yagnik

Matthew E Kulick

Maureen Heymans

Ryan Michael Rifkin

Monica Chawathe Lenart

Rasmus Munk Larsen

Jinan Lou

Sarvjeet Singh

Sumitro Samaddar

Yi-Fan Chen

Anurag Vyas

John Roberts Anderson

Yanli Cai

Arsalan Mosenia

Techniques of providing category recommendations include a category recommendation system that provides users recommended categories based on implicit data (e.g., user-item interactions) and/or explicit data (e.g., queries, user information). The recommendations can be personalized or non-personalized (i.e., depending if user embeddings are used), queried or non-queried (i.e., depending on whether query embeddings are used), or personalized and queried (if both user and query embeddings are used). In any of these cases, there is an offline mode and a serving mode. In the offline mode, a category embedding is generated from an aggregation of item embeddings associated with a candidate category. In the serving mode, the candidate category is selected for display on a user device based on a similarity between the category embedding and either, or both, of the user embedding and the query embedding.

Category recommendation with implicit item feedback

Methods and apparatus for generating, in response to a search query of a user, a personal search result that identifies a physical location previously interacted with by the user, such as a physical location previously visited by the user. Some implementations are directed to methods and apparatus for determining whether a search query is a personal locational query indicating a desire for information related to one or more physical locations previously interacted with by the user. Some implementations are directed to methods and apparatus for determining search parameters for such a personal locational query. Some implementations are directed to methods and apparatus for searching private content of the user to determine physical locations previously interacted with by the user that are responsive to such a personal locational query. Some implementations are directed to methods and apparatus for generating personal search results that identify determined physical locations previously interacted with by the user.

Personal search result identifying a physical location previously interacted with by a user

Implementations include systems and methods for scoring candidates for set recommendation problems. An example method includes repeating, for each code in code arrays for items in a set of items, determining a most common value for the code. In some implementations, the method includes determining that the most common value occurs with a frequency that meets an occurrence threshold and adding the code and the most common value to set-inclusion criteria. In other implementations, the method includes determining a value for the code from a code array for a seed item and adding the code and the most common value to set-inclusion criteria when the value for the code from the code array for the seed item matches the most common value. The method may also include evaluating a similarity with a candidate item based on the set-inclusion criteria and basing a recommendation regarding the candidate item on the similarity.

Scoring candidates for set recommendation problems

Techniques for using social networking information may include making a determination that content authored by a first party references a second party more than a specified number of times. An adjustment in affinity may be made between the first party and the second party in the social network in response to the determination. Making the adjustment in affinity may include modifying a social graph of at least the first party that is referenced by the social network in order to reflect the adjustment in affinity. Modifying the social graph includes creating a closer connection between the first party and the second party than had existed prior to the determination. In response to the adjustment in the affinity, promotion is adjusted, to the first party, of content in the social network that has a connection to the second party.

Using social networking information

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Santa Clara, CA, United States of America

Anandsudhakar Kesari