US-12625897-B2 - Performance metric prediction and content item text suggestion based upon content item text

US12625897B2US 12625897 B2US12625897 B2US 12625897B2US-12625897-B2

Abstract

One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Inventors

Shaunak MISHRA
Hua-Ying Tsai
Kamil Michal Zasadzinski
Wei Yu Lin
Yu Tian
Changwei Hu
Kevin Yen
Manisha Verma
Yifan Hu
Maxim Ivanovich Sviridenko
Avinash Chukka
Max Edward Beech
Chao-Hung Wang

Assignees

YAHOO ASSETS LLC

Dates

Publication Date: 20260512
Application Date: 20240130

Claims (20)

1 . A method, comprising: displaying a content item text interface via a client device; receiving, via the content item text interface, a first set of content item text; determining, based upon the first set of content item text received via the content item text interface, a first performance metric score associated with performance of the first set of content item text; determining a first similarity score associated with a similarity between the first set of content item text and a second set of content item text; selecting, based upon the first similarity score, one or more sets of content item text associated with one or more performance metric scores that are higher than the first performance metric score; determining a text strength classification of the first set of content item text based upon the first performance metric score determined based upon the first set of content item text received via the content item text interface; and displaying, via the client device, at least one of one or more representations of the one or more sets of content item text or an indication of the text strength classification.
2 . The method of claim 1 , wherein: the determining the first performance metric score is performed using a first machine learning model.
3 . The method of claim 2 , comprising: training a machine learning model using first training data to generate the first machine learning model, wherein the first training data comprises a plurality of sets of content item text associated with a plurality of content items and a plurality of sets of content event information associated with the plurality of content items.
4 . The method of claim 3 , wherein: the plurality of sets of content event information comprises a first set of content event information associated with a second content item; and the first set of content event information is indicative of first content event information associated with first content events performed via one or more first internet resources corresponding to a first entity, wherein a content event of the first content events corresponds to presentation of the second content item via an internet resource of the one or more first internet resources.
5 . The method of claim 4 , comprising: receiving, via the content item text interface, an indication of the first entity, wherein the determining the first performance metric score is performed based upon the first entity.
6 . The method of claim 3 , wherein: the first machine learning model comprises at least one of a linear model, a logistic regression model, a naïve Bayes logistic regression (NBLR) model or a deep learning model.
7 . The method of claim 1 , comprising: determining a first embedding-based representation of the first set of content item text.
8 . The method of claim 1 , wherein: the displaying the one or more representations of the one or more sets of content item text is performed responsive to a determination that each performance metric score of the one or more performance metric scores meets a threshold performance metric score.
9 . The method of claim 1 , wherein the selecting the one or more sets of content item text comprises: selecting a plurality of sets of content item text based upon a determination that the plurality of sets of content item text are associated with highest similarity scores of a plurality of similarity scores; and selecting the one or more sets of content item text from among the plurality of sets of content item text based upon a determination that each performance metric score of the one or more performance metric scores meets a threshold performance metric score.
10 . The method of claim 1 , comprising: displaying one or more indications of the one or more performance metric scores associated with the one or more sets of content item text.
11 . The method of claim 1 , comprising: displaying one or more indications that the one or more performance metric scores associated with the one or more sets of content item text are higher than the first performance metric score.
12 . The method of claim 1 , wherein: the displaying the indication of the text strength classification is performed responsive to a determination that each performance metric score of the one or more performance metric scores meets a threshold performance metric score.
13 . The method of claim 1 , comprising: receiving a third set of content item text; determining, based upon the third set of content item text, a second performance metric score; and determining, based upon the second performance metric score, a second text strength classification of the third set of content item text.
14 . The method of claim 13 , comprising: displaying an indication of the second text strength classification.
15 . A computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising: displaying a content item text interface via a client device; receiving, via the content item text interface, a first set of content item text; determining, based upon the first set of content item text, a first performance metric score; determining a first similarity score associated with a similarity between the first set of content item text and a second set of content item text; selecting, based upon the first similarity score, one or more sets of content item text associated with one or more performance metric scores that are higher than the first performance metric score; determining a text strength classification of the first set of content item text based upon the first performance metric score; and responsive to a determination that each performance metric score of the one or more performance metric scores meets a threshold performance metric score, displaying, via the client device, an indication of the text strength classification.
16 . The computing device of claim 15 , wherein: the determining the first performance metric score is performed using a first machine learning model.
17 . The computing device of claim 16 , the operations comprising: training a machine learning model using first training data to generate the first machine learning model, wherein the first training data comprises a plurality of sets of content item text associated with a plurality of content items and a plurality of sets of content event information associated with the plurality of content items.
18 . The computing device of claim 17 , wherein: the plurality of sets of content event information comprises a first set of content event information associated with a second content item; and the first set of content event information is indicative of first content event information associated with first content events performed via one or more first internet resources corresponding to a first entity, wherein a content event of the first content events corresponds to presentation of the second content item via an internet resource of the one or more first internet resources.
19 . The computing device of claim 18 , the operations comprising: receiving, via the content item text interface, an indication of the first entity, wherein the determining the first performance metric score is performed based upon the first entity.
20 . A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising: receiving, from a client device, a first set of content item text; determining, based upon the first set of content item text, a first performance metric score associated with performance of the first set of content item text; determining a first similarity score associated with a similarity between the first set of content item text and a second set of content item text; selecting, based upon the first similarity score, one or more sets of content item text associated with one or more performance metric scores that are higher than the first performance metric score; determining a text strength classification of the first set of content item text based upon the first performance metric score determined based upon the first set of content item text; and displaying, via the client device, at least one of one or more representations of the one or more sets of content item text or an indication of the text strength classification.

Description

RELATED APPLICATION This application claims priority to and is a continuation of U.S. application Ser. No. 17/314,137, filed on May 7, 2021, entitled “PERFORMANCE METRIC PREDICTION AND CONTENT ITEM TEXT SUGGESTION BASED UPON CONTENT ITEM TEXT”, which is incorporated by reference herein in its entirety. BACKGROUND Many services, such as websites, applications, etc. may provide platforms for viewing media. For example, a user may interact with a service. While interacting with the service, selected media may be presented to the user automatically. Some of the media may be advertisements advertising products and/or services associated with a company. SUMMARY In accordance with the present disclosure, one or more computing devices and/or methods are provided. In an example, a content item text interface may be displayed via a client device. A first set of content item text may be received via the content item text interface. A first performance metric score may be determined based upon the first set of content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined based upon the first set of content item text and the plurality of sets of content item text associated with a plurality of content items. The plurality of sets of content item text may comprise a second set of content item text of a first content item of the plurality of content items. A first similarity score of the plurality of similarity scores may be associated with a similarity between the first set of content item text and the second set of content item text. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. A content item text suggestion interface comprising one or more representations of the one or more sets of content item text may be displayed via the client device. In an example, a first set of content item text may be received from a client device. A first performance metric score may be determined based upon the first set of content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined based upon the first set of content item text and the plurality of sets of content item text associated with a plurality of content items. The plurality of sets of content item text may comprise a second set of content item text of a first content item of the plurality of content items. A first similarity score of the plurality of similarity scores may be associated with a similarity between the first set of content item text and the second set of content item text. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed via the client device. DESCRIPTION OF THE DRAWINGS While the techniques presented herein may be embodied in alternative forms, the particular embodiments illustrated in the drawings are only a few examples that are supplemental of the description provided herein. These embodiments are not to be interpreted in a limiting manner, such as limiting the claims appended hereto. FIG. 1 is an illustration of a scenario involving various examples of networks that may connect servers and clients. FIG. 2 is an illustration of a scenario involving an example configuration of a server that may utilize and/or implement at least a portion of the techniques presented herein. FIG. 3 is an illustration of a scenario involving an example configuration of a client that may utilize and/or implement at least a portion of the techniques presented herein. FIG. 4 is a flow chart illustrating an example method for determining a performance metric score of a set of content item text and/or suggesting one or more sets of content item text based upon the set of content item text. FIG. 5A is a component block diagram illustrating an example system for determining a performance metric score of a set of content item text and/or suggesting one or more sets of content item text based upon the set of content item text, where a content item text interface is display