Facebook, Inc.

Meta Platforms, Inc.

Zheng Chen

Shyamsundar Rajaram

Pradheep K Elango

Yang Li

Xin Liu

Raghavendra Rao Donamukkala

Nimish Rameshbhai Shah

Leon R Cho

Toby Jonas F Roessingh

Lixing Huang

An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.

Conversion optimization with long attribution window

The present disclosure is directed toward systems and methods for optimizing view-through conversion rates. For example, systems and methods described herein train and utilize a machine learning model that predicts whether providing a digital impression to a particular networking system user will result in a conversion. Systems and methods described herein identify view-through conversions by generating a vector associated with the provision of a digital impression to a networking system user and receiving third-party conversion information during an attribution window. The systems and methods described herein then utilize the vector and conversion information to train the machine learning model to predict future conversions.

Optimizing conversion rates for impressions provided in a networking system

An online system obtains a composite prediction associated with a content item indicating a likelihood that a viewing user of the online system will perform a type of conversion associated with the content item via one or more paths of events leading to the type of conversion. The online system obtains the composite prediction based on a tree data structure describing the path(s) of events. Upon identifying an opportunity to present content to the viewing user, the online system identifies the tree data structure corresponding to the type of conversion from multiple tree data structures maintained in the online system and obtains a composite prediction associated with the content item by evaluating and performing operations associated with nodes of the tree data structure while traversing the nodes. The online system then determines whether to present the content item to the viewing user based on the composite prediction.

Obtaining a composite prediction indicating a likelihood that a user of an online system will perform a conversion associated with a content item via one or more paths of events

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Mountain View, CA, United States of America

Zheng Chen