Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal content item.

Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal content item.

Abstract: Online system users interact with one or more third party systems, with the online system maintaining an account for each of its users and each third party system maintaining a third party account for each of its users. The online system compares information in a user's account to accessible information in third party accounts and establishes connections between the user's account and third party accounts based on the comparisons, a connection including a confidence level indicating a likelihood of a third party account being associated with the user of the online system corresponding to the user's account. Similarly, the online system compares information in different third party accounts and establishes connections between different third party accounts based on the comparisons including includes a confidence level indicating a likelihood of a third party account and an additional third party account being associated with the same user.

Abstract: Online system users interact with one or more third party systems, with the online system maintaining an account for each of its users and each third party system maintaining a third party account for each of its users. The online system compares information in a user's account to accessible information in third party accounts and establishes connections between the user's account and third party accounts based on the comparisons, a connection including a confidence level indicating a likelihood of a third party account being associated with the user of the online system corresponding to the user's account. Similarly, the online system compares information in different third party accounts and establishes connections between different third party accounts based on the comparisons including includes a confidence level indicating a likelihood of a third party account and an additional third party account being associated with the same user.

Abstract: A computer-implemented method and system for Web search ranking are provided herein. The method includes generating a number of training samples from clickthrough data, wherein the training samples include positive query-document pairs and negative query-document pairs. The method also includes discriminatively training a translation model based on the training samples and ranking a number of documents for a Web search based on the translation model.

Abstract: A computer-implemented method and system for Web search ranking are provided herein. The method includes generating a number of training samples from clickthrough data, wherein the training samples include positive query-document pairs and negative query-document pairs. The method also includes discriminatively training a translation model based on the training samples and ranking a number of documents for a Web search based on the translation model.