Identifying content items in response to a text-based request

Patent metadata

Field	Value
Publication number	US-2025307306-A1
Application number	US-202519234699-A
Country	US
Kind code	A1
Filing date	Jun 11, 2025
Priority date	Aug 20, 2020
Publication date	Oct 2, 2025
Grant date	—

Abstract

Systems and methods for responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.

First claim

What is claimed:

1 . A method comprising:
generating a collection of training data comprising: obtaining a plurality of text-based requests and a plurality of content items, each text-based request of the plurality of text-based requests being associated with one or more content items of the plurality of content items; and for each text-based request:
generating a representative embedding vector for the text-based request;
projecting the one or more content items associated with the text-based request into an content item embedding space;
clustering the projected one or more content items into a neighborhood of positive representations of the text-based request;
generating an instance of positive training data comprising the text-based request, the representative embedding vector, and cluster data; and
generating one or more instances of negative training data comprising an embedding vector projected into the content item embedding space outside of the cluster;
training an embedding vector generator using the collection of training data, the embedding vector generator configured to generate embedding vectors into a content item embedding space for a text-based request; and generating, in response to a first text-based request received from a user, an embedding vector for the first text-based request in the content item embedding space.

2 . The method of claim 1 , wherein generating the representative embedding vector for each text-based request further comprises:
generating a set of one or more word pieces from the text-based request; generating one or more word piece embedding vectors, each word piece embedding vector corresponding to a respective word piece of the one or more word pieces; and generating the representative embedding vector from the one or more word piece embedding vectors.

3 . The method of claim 2 , wherein generating the representative embedding vector from the one or more word piece embedding vectors comprises averaging the one or more word piece embedding vectors.

4 . The method of claim 1 , wherein the cluster data comprise a centroid fort the cluster and dimensional information of the cluster.

5 . The method of claim 1 , wherein obtaining the collection of the plurality of text-based requests and the plurality of content items comprises:
obtaining a collection of text-based request and content item pairs, each pair corresponding to a text-based request by a user and an content item with which the user interacted; and aggregating the collection of text-based request and content item pairs so that each text-based request is associated with one or more of the content items.

6 . The method of claim 1 , further comprising:
pre-generating a collection of embedding vectors from a collection of text-based requests, the pre-generating comprising: for each text-based request of the collection of text-based requests:
generating one or more word pieces for the text-based request;
generating a representative embedding vector for the text-based request from the word pieces;
generating a request embedding vector that projects the representative embedding vector into the content item embedding space; and
storing the text-based request and the generated request embedding vector.

7 . The method of claim 1 , further comprising:
maintaining a plurality of content items, wherein the plurality of content items are associated with a plurality of content item embedding vectors that project the plurality of content items into an content item embedding space; in response to a text-based query, projecting a request embedding vector generated from the text-based query into the content item embedding space; determining, based at least on the plurality of content item embedding vectors and the request embedding vector, an content item from the plurality of content items; and providing the content item in response to the text-based request.

8 . A system comprising:
one or more processors; and a memory storing program instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: generating a collection of training data comprising: obtaining a plurality of text-based requests and a plurality of content items, each text-based request of the plurality of text-based requests being associated with one or more content items of the plurality of content items; and for each text-based request:
generating a representative embedding vector for the text-based request;
projecting the one or more content items associated with the text-based request into an content item embedding space;
clustering the projected one or more content items into a neighborhood of positive representations of the text-based request;
generating an instance of positive training data comprising the text-based request, the representative embedding vector, and cluster data; and
generating one or more instances of negative training data comprising an embedding vector projected into the content item embedding space outside of the cluster;
training an embedding vector generator using the collection of training data, the embedding vector generator configured to generate embedding vectors into a content item embedding space for a text-based request; and generating, in response to a first text-based request received from a user, an embedding vector for the first text-based request in the content item embedding space.

9 . The system of claim 8 , wherein generating the representative embedding vector for each text-based request further comprises:
generating a set of one or more word pieces from the text-based request; generating one or more word piece embedding vectors, each word piece embedding vector corresponding to a respective word piece of the one or more word pieces; and generating the representative embedding vector from the one or more word piece embedding vectors.

10 . The system of claim 9 , wherein generating the representative embedding vector from the one or more word piece embedding vectors comprises averaging the one or more word piece embedding vectors.

11 . The system of claim 8 , wherein the cluster data comprise a centroid fort the cluster and dimensional information of the cluster.

12 . The system of claim 8 , wherein obtaining the collection of the plurality of text-based requests and the plurality of content items comprises:
obtaining a collection of text-based request and content item pairs, each pair corresponding to a text-based request by a user and an content item with which the user interacted; and aggregating the collection of text-based request and content item pairs so that each text-based request is associated with one or more of the content items.

13 . The system of claim 8 , wherein the program instructions further include instructions that, when executed by the one or more processors, further cause the one or more processors to perform operations comprising:
pre-generating a collection of embedding vectors from a collection of text-based requests, the pre-generating comprising: for each text-based request of the collection of text-based requests:
generating one or more word pieces for the text-based request;
generating a representative embedding vector for the text-based request from the word pieces;
generating a request embedding vector that projects the representative embedding vector into the content item embedding space; and
storing the text-based request and the generated request embedding vector.

14 . The system of claim 8 , wherein the program instructions further include instructi

Assignees

• Pinterest Inc

Inventors

• Pancha Nikil
• Zhai Andrew Huan
• Rosenberg Charles Joseph

Classifications

• G06F16/45

  Clustering; Classification · CPC title

• G06F16/444

  Spatial browsing, e.g. 2D maps, 3D or virtual spaces · CPC title

• G06F40/56

  Natural language generation · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title