公开(公告)号：US20190171689A1

公开(公告)日：2019-06-06

申请号：US16211209

申请日：2018-12-05

Applicant: GOOGLE LLC

Inventor： Ahmed Kachkach ,  Yi-fan Chen ,  Karthik Lakshmanan ,  Sally Goldman ,  Puneet Chopra ,  Radhika Malpani ,  Jeremy Shute ,  Berna Erol

IPC: G06F16/9538 ,  G06F3/0482 ,  G06F16/2457 ,  G06F16/28 ,  G06F16/9535

Abstract: A search computing system receives an interaction history for one or more respective users describing interactions with one or more items and generates a co-interaction matrix, each value in the co-interaction matrix representing a number of common users determined to have had the interaction with an item of a corresponding row and an item of the corresponding column of the co-interaction matrix where the value is located. The search computing system generates an embeddings matrix comprising an item embedding value for each of the one or more items by applying matrix factorization to the co-interaction matrix and determines, in response to a search query of a particular user, a user embedding value for the searching user based on the interaction history for the searching user. The search computing system determines a similarity between each search result and user interaction history by comparing the user embedding value against each of the item embedding values.

公开(公告)号：US20190012719A1

公开(公告)日：2019-01-10

申请号：US16129508

申请日：2018-09-12

Applicant: GOOGLE LLC

Inventor： John Roberts Anderson ,  Ryan Michael Rifkin ,  Jay Yagnik ,  Rasmus Larsen ,  Sarvjeet Singh ,  Yi-fan Chen ,  Anandsudhakar Kesari

IPC: G06Q30/06 ,  G06Q30/02

Abstract: 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.