DETERMINING VIEWABILITY OF CONTENT ITEMS DISPLAYED ON CLIENT DEVICES BASED ON USER INTERACTIONS
An online system predicts viewability of content items based on user interactions associated with the content items. The online system sends content items for display via client devices. The online system receives a request for a report based on viewability of the content item. The online system receives user interactions with the content item and determines a value of a user interaction metric based on the received user interactions. The online system provides the value of the user interaction metric as input to a correlation model to predict a value of the viewability metric for the content items. The online system may generates report based on the predicted viewability metric value.
1 . A computer-implemented method comprising:
sending one or more content items for display via client devices;
receiving a request for a report based on viewability of the one or more content items, the viewability comprising a viewability metric representing a likelihood that a user viewed the one or more content items;
receiving user interactions associated with a content item;
determining a user interaction metric representing a rate of user interactions with the content item;
providing the value of the user interaction metric as input to a correlation model configured to receive an input value of the user interaction metric and predict a corresponding value of the viewability metric; and
predicting a value of the viewability metric for the one or more content items using the correlation model; and
generating the requested report based on the predicted value of the viewability metric.
2 . The computer-implemented method of claim 1 , further comprising:
sending a plurality of content items for display via client devices;
receiving user interactions and viewability signals associated with the plurality of content items;
determining values of user interaction metric and corresponding values of viewability metric based on the receiving user interactions and viewability signals; and
generating the correlation model based on the determined values of user interaction metric and corresponding values of viewability metric.
3 . The computer-implemented method of claim 2 , wherein generating the correlation model comprises curve fitting based on the determined values of user interaction metric and corresponding values of viewability metric.
4 . The computer-implemented method of claim 2 , wherein the correlation model is a polynomial function that maps values of user interaction metric to values of viewability metric.
5 . The computer-implemented method of claim 2 , wherein the correlation model is a linear function that maps values of user interaction metric to values of viewability metric.
6 . The computer-implemented method of claim 1 , wherein the user interaction metric represents a click through rate (CTR).
7 . The computer-implemented method of claim 1 , wherein the viewability metric is based on a size of a portion of a particular content item displayed on a display screen of the client device.
8 . The computer-implemented method of claim 1 , wherein the viewability metric is based on an amount of time that the content item was presented on a display screen of a particular client device.
9 . The computer-implemented method of claim 1 , wherein the correlation model is for a content provider, and wherein the one or more content items for generating the model are associated with the content provider.
10 . The computer-implemented method of claim 1 , wherein the correlation model is for a placement within a web page, and wherein the one or more content items for generating the model are associated with the placement on the web page.
11 . The computer-implemented method of claim 1 , wherein the generated report describes results of an online campaign for sending content items to users.
12 . The computer-implemented method of claim 1 , wherein the generated report ranks content distribution across content providers.
13 . A non-transitory computer-readable medium storing instructions, wherein the stored instructions are for:
sending one or more content items for display via client devices;
receiving a request for a report based on viewability of the one or more content item, the viewability comprising a viewability metric representing a likelihood that a user viewed the content;
receiving user interactions associated with a content item;
determining a user interaction metric representing a rate of user interactions with the content item;
providing the value of the user interaction metric as input to a correlation model configured to receive an input value of the user interaction metric and predict a corresponding value of the viewability metric; and
predicting a value of the viewability metric for the one or more content items using the correlation model; and
generating the requested report based on the predicted value of the viewability metric.
14 . The non-transitory computer-readable medium of claim 13 , wherein the stored instructions further comprise instructions for:
sending a plurality of content items for display via client devices;
receiving user interactions and viewability signals associated with the plurality of content items;
determining values of user interaction metric and corresponding values of viewability metric based on the receiving user interactions and viewability signals; and
generating the correlation model based on the determined values of user interaction metric and corresponding values of viewability metric.
15 . The non-transitory computer-readable medium of claim 14 , wherein instructions for generating the correlation model comprise instructions for curve fitting based on the determined values of user interaction metric and corresponding values of viewability metric.
16 . The non-transitory computer-readable medium of claim 13 , wherein the correlation model is a polynomial function that maps values of user interaction metric to values of viewability metric.
17 . The non-transitory computer-readable medium of claim 13 , wherein the user interaction metric represents a click through rate (CTR).
18 . The non-transitory computer-readable medium of claim 13 , wherein the correlation model is for a content provider, and wherein the one or more content items for generating the model are associated with the content provider.
19 . The non-transitory computer-readable medium of claim 13 , wherein the correlation model is for a placement within a web page, and wherein the one or more content items for generating the model are associated with the placement on the web page.
20 . A computer system comprising:
one or more computer processors; and
a non-transitory computer-readable medium storing instructions, wherein the stored instructions are for:
sending one or more content items for display via client devices;
receiving a request for a report based on viewability of the one or more content item, the viewability comprising a viewability metric representing a likelihood that a user viewed the content;
receiving user interactions associated with a content item;
determining a user interaction metric representing a rate of user interactions with the content item;
providing the value of the user interaction metric as input to a correlation model configured to receive an input value of the user interaction metric and predict a corresponding value of the viewability metric; and
predicting a value of the viewability metric for the one or more content items using the correlation model; and
generating the requested report based on the predicted value of the viewability metric.