FACEBOOK, INC. (Menlo Park, CA)

The current disclosure is directed towards techniques and systems for optimizing conversion rates for views. In particular, the systems and methods discussed herein train and use an algorithm that determines whether giving a digital impression to a specific networking user results in a conversion. The methods and systems discussed herein can detect conversions that are view-through by creating an image vector associated with the providing of a digital impression for a user on a networking system and then receiving third-party conversion data in an attribution window. The methods and systems described herein utilize the vector as well as the information about conversion to build a machine learning model to predict future conversions.

Optimization of conversion rates is an essential concern for most businesses. For example, a company generally wishes its digital impressions (e.g. digital advertisements) to be made available to people who are most likely to complete a desired action after seeing the digital impression. If a business sells widgets, it will prefer that its digital impressions be seen by the most likely customers to buy the widget. For instance, the purchase action refers to the kind of conversion which the company is the most interested in. Another instance of an action in which an organization is the most concerned is simply driving traffic to one or more of its web pages. In that example the business would like its digital impression provided to and seen by those who are likely to visit one or more of the targeted web pages in response to viewing the digital impression.

Many conventional optimization systems are focused on conversion rates which “click through”, regardless of the intended conversion. The conventional system could give a digital impression to users and monitor the clicks they make. Conventional systems do not consider other crucial cross-sections of the user that’s actions to a digital impression lead to a conversion even although the user did not click on the impression. In an online setting, in which users are able to view several digital impressions at once, there might be a gap in time between the time the person views the impression and when they go to a website, purchase a product or do any other activity connected to the impression. A user might view the digital image for a brief time, before entering the URL for the page that corresponds to it an hour later. Conventional systems won’t recognize the digital impression the user has previously seen as the reason for conversion, in the event that the user interacts with the website. Furthermore, the conventional systems fail to account for such additionalconversions when optimizing delivery of future digital impressions.

Therefore, there is a need for a system that accounts for conversion rates that are associated with a wider range of user interactions relative to a digital impression.

One or more embodiments described herein provide benefits and/or solve one or more of the foregoing or other problems in the art with systems and methods for optimizing conversion rates based on digital impression views (“view-throughconversion”) in addition to digital impression clicks (“click-through conversion”). In one or more embodiments the systems and techniques described herein monitor conversions through view along with click-through conversions and use that information to create a machine-learning model that can predict the future likelihood of conversions for both kinds of interactions. These systems and methods enable companies to give digital impressions to those who are likely take part in activities that could result in a conversion.

Additional features and advantages of the present invention will be set forth in the following description In part, the advantages and features will be evident from the description, or may be learned by practicing these exemplary embodiments. These features and advantages are achievable using the combinations and instruments specifically mentioned in the attached claims. These and other features are made more clear by the appended claims and the following description. It is also possible to learn them through the practice of the exemplary embodiments set forth below.

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