Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments are disclosed for predicting target events occurrence for an advertisement campaign. A computing device according to some embodiments assigns a label to an advertisement as unlabeled, in response to a notification that a prerequisite event occurs for the advertisement. The device generates feature vectors based on data that relate to the advertisement. The device further trains a machine learning model using the feature vectors of the unlabeled advertisement based on a first term of an objective function, without waiting for a target event for the advertisement to occur. The first term depends on unlabeled advertisements. The device predicts a probability of a target event occurring for a new advertisement, by feeding data of the new advertisement to the trained machine learning model.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about content provided by one or more third party systems accessed by the user and determines products associated with accessed content. When the online system identifies an opportunity to present to a user, the online system identifies products for inclusion in the content item and identifies candidate products for inclusion in the content item based on products previously accessed by the user. The online system selects a product of the candidate products based on probabilities of the user accessing content items including different candidate products. The online system includes the content item having information about the selected product in one or more selection processes that select content for presentation to the user.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about products associated one or more third party systems accessed by users of the online system. When the online system identifies an opportunity to present to a user, the online system identifies candidate products for inclusion in the content item based on products previously accessed by the users. For example, the online system identifies candidate products based on products accessed by the user and by one or more other users. The online system may include differing levels of information about a selected candidate product in the content item. In various embodiments, the online system determines a level of information about the selected candidate product based on products previously accessed by the user.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about products associated one or more third party systems accessed by users of the online system. When the online system identifies an opportunity to present to a user, the online system identifies candidate products for inclusion in the content item based on products previously accessed by the users. For example, the online system identifies candidate products based on products accessed by the user and by one or more other users. Based on likelihoods of the user accessing content items including different candidate products, the online system selects a candidate product and includes the content item having information about the selected candidate product in one or more selection processes that select content for presentation to the user.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about content provided by one or more third party systems the user accessed and determines products associated with accessed content. When the online system identifies an opportunity to present to a user, the online system retrieves products maintained by the online system and identifies candidate products for inclusion in the content item based on relevance of the products to the user. The online system determines probabilities of the user accessing the content item including different candidate products and removes combinations of the content item and candidate products having less than a threshold probability of user interaction. The online system includes one or more combinations of the content item and candidate products in one or more selection processes selecting content for presentation to the user.

Abstract: 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.

Abstract: Embodiments are disclosed for predicting target events occurrence for an advertisement campaign. A computing device according to some embodiments assigns a label to an advertisement as unlabeled, in response to a notification that a prerequisite event occurs for the advertisement. The device generates feature vectors based on data that relate to the advertisement. The device further trains a machine learning model using the feature vectors of the unlabeled advertisement based on a first term of an objective function, without waiting for a target event for the advertisement to occur. The first term depends on unlabeled advertisements. The device predicts a probability of a target event occurring for a new advertisement, by feeding data of the new advertisement to the trained machine learning model.