Abstract: In one embodiment, a method includes receiving data comprising network addresses, processing the received data based on veracity associated with the data, generating predicted locations of each network address based on the processed data, wherein the predicted locations are based on the veracity associated with the received data, determining a weight for each predicted location based on a time stamp associated with the predicted location that represents an age of the predicted location, wherein the weight represents a probability that the predicted location corresponds to a true location of the corresponding network address, and providing, in response to a request to identify a geographic location for a particular network address, one or more of the predicted locations that correspond to the particular network address based on the respective weights of the predicted locations.

Abstract: In one embodiment, a method includes generating predicted locations of each of a plurality of network addresses, wherein each predicted location is associated with a time stamp representing an age of the predicted location, determining a weighting factor representing a probability that at least one of the predicted locations of the network address corresponds to a true location of the network address based on location-related features associated with each network address and the time stamps, determining a weight for each predicted location based on at least the weighting factor, wherein the weight represents a probability that the predicted location corresponds to the true location of the network address, and providing one or more of the predicted locations that correspond to a particular network address based on the respective weights of the predicted locations in response to a request to identify a geographic location for the particular network address.

Abstract: An online system receives a request from an online system user to present a content item associated with an action that may be performed on a third party website not associated with the user. The online system identifies a set of third party websites on which the action may be performed based on information provided by content publishers associated with the websites describing performances of the action on the websites. The online system predicts a likelihood a viewing user of the online system presented with the content item will perform the action on each third party website based on the information provided by the content publishers and selects a website associated with a highest predicted likelihood the viewing user will perform the action on the website. The online system generates the content item including a link to the selected website and provides the content item for presentation.

Abstract: In one embodiment, a method includes generating predicted locations of each of a plurality of network addresses, wherein each predicted location is associated with a time stamp representing an age of the predicted location, determining a weighting factor representing a probability that at least one of the predicted locations of the network address corresponds to a true location of the network address based on location-related features associated with each network address and the time stamps, determining a weight for each predicted location based on at least the weighting factor, wherein the weight represents a probability that the predicted location corresponds to the true location of the network address, and providing one or more of the predicted locations that correspond to a particular network address based on the respective weights of the predicted locations in response to a request to identify a geographic location for the particular network address.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating an identity resolution model from a ground truth data set to accurately match users across one or more digital content providers to perform analyses of user activities across the one or more digital content providers. For example, the systems described herein can generate a ground truth data set of known users and utilize the ground truth data set to generate an identity resolution model for one or more digital content providers based on predicted user identities. Furthermore, in one or more embodiments, the systems utilize the identity resolution model to accurately resolve and match user identities between one or more digital content providers and assign universal identifiers to the user identities. Moreover, the disclosed systems can utilize the universal identifiers to provide analytical insights of user actions between the one or more digital content providers.

Abstract: In one embodiment, a method includes receiving one or more communication network addresses and one or more geographic locations of each network address, determining one or more location-related features based on each network address, generating one or more predicted locations of the network address, each predicted location corresponding to one of the first geographic locations of the network address, and each predicted location being associated with a time stamp representing an age of the predicted location, determining, based on the location-related features and the time stamps, a weighting factor representing a probability that at least one of the predicted locations of the network address corresponds to a true location of the network address, and determining, for each of the predicted locations, a weight based on at least the weighting factor, wherein the weight represents a probability that the predicted location corresponds to the true location of the network address.

Abstract: Embodiments include one or more client devices accessible by users, an online system, and one or more partner systems such that the online system is able to identify a user of the online system across different devices and browsers based on the user activity that occurs external to the online system. A user performs user actions (e.g. purchase a product) on a web page of a partner system and may provide personally identifiable information (PII) to the partner system. The partner system provides the hashed PII and user actions performed by the user to the online system. The online system identifies a user profile on the online system by matching personal information in the user profile to the hashed PII. The online system generates a confidence score indicating a likelihood that the identified user of the online system is the individual that performed the external user action.

Abstract: In one embodiment, a method includes analyzing social graph information associated with users of a social-networking system, developing feature vectors describing elements of social graph information, and applying the feature vectors to determine the relevance of elements of social graph information to the location of special relevance. The method further includes receiving at least one data point from a user's networked device, applying the feature vectors to the at least one data point to determine the relevance of the at least one data point to the location of special relevance, and assigning weight to each data point based on the determined relevance of each data point to the location of special relevance. Finally, the method includes processing the at least one data point according to its assigned weight and forming a prediction, to a particular degree of certainty, indicating the user's location of special relevance.

Abstract: In one embodiment, a method includes receiving one or more communication network addresses and one or more geographic locations of each network address, determining one or more location-related features based on each network address, generating one or more predicted locations of the network address, each predicted location corresponding to one of the first geographic locations of the network address, and each predicted location being associated with a time stamp representing an age of the predicted location, determining, based on the location-related features and the time stamps, a weighting factor representing a probability that at least one of the predicted locations of the network address corresponds to a true location of the network address, and determining, for each of the predicted locations, a weight based on at least the weighting factor, wherein the weight represents a probability that the predicted location corresponds to the true location of the network address.

Abstract: An online system receives a request from an online system user to present a content item associated with an action that may be performed on a third party website not associated with the user. The online system identifies a set of third party websites on which the action may be performed based on information provided by content publishers associated with the websites describing performances of the action on the websites. The online system predicts a likelihood a viewing user of the online system presented with the content item will perform the action on each third party website based on the information provided by the content publishers and selects a website associated with a highest predicted likelihood the viewing user will perform the action on the website. The online system generates the content item including a link to the selected website and provides the content item for presentation.

Abstract: An online system predicts household features of a user, e.g., household size and demographic composition, based on image data of the user, e.g., profile photos, photos posted by the user and photos posted by other users socially connected with the user, and textual data in the user's profile that suggests relationships among individuals shown in the image data of the user. The online system applies one or more models trained using deep learning techniques to generate the predictions. For example, a trained image analysis model identifies each individual depicted in the photos of the user; a trained text analysis model derive household member relationship information from the user's profile data and tags associated with the photos. The online system uses the predictions to build more information about the user and his/her household in the online system, and provide improved and targeted content delivery to the user and the user's household.

Abstract: Embodiments include one or more client devices accessible by users, an online system, and one or more partner systems such that the online system is able to identify a user of the online system across different devices and browsers based on the user activity that occurs external to the online system. A user performs user actions (e.g. purchase a product) on a web page of a partner system and may provide personally identifiable information (PII) to the partner system. The partner system provides the hashed PII and user actions performed by the user to the online system. The online system identifies a user profile on the online system by matching personal information in the user profile to the hashed PII. The online system generates a confidence score indicating a likelihood that the identified user of the online system is the individual that performed the external user action.

Abstract: A method is disclosed for attributing conversions among multiple members of a socially connected influence group, such as a household. Data from advertising impressions, including views and clicks, is maintained by an online system. When a conversion is made, the social network of the user creating the conversion event is analyzed. An influence group, defined as a group comprising the users and group of socially connected users whom influence the purchasing decisions of the first user, is created. Conversion data is analyzed for the first user and the other members of the influence group. This data is weighted to determine the propensities of successful conversions among all members of the influence group.

Abstract: An online system determines an effect of presenting a content item in causing users to perform a specific action associated with the content item without excluding presentation of content item from certain users. The online system presents the content item to various users and identifies a set of users not presented with the content item. Based on probabilities of presenting the content item to users of the set and to users to whom the content item was presented, the online system weights users of the set so a distribution of probabilities of being presented with the content item for the set matches a distribution of probabilities of the content item being presented to the users who were presented with the content item. The online system determines a metric based on the weights and occurrences of the specific action by users of the set and users presented with the content item.

Abstract: An online system predicts household features of a user, e.g., household size and demographic composition, based on image data of the user, e.g., profile photos, photos posted by the user and photos posted by other users socially connected with the user, and textual data in the user's profile that suggests relationships among individuals shown in the image data of the user. The online system applies one or more models trained using deep learning techniques to generate the predictions. For example, a trained image analysis model identifies each individual depicted in the photos of the user; a trained text analysis model derive household member relationship information from the user's profile data and tags associated with the photos. The online system uses the predictions to build more information about the user and his/her household in the online system, and provide improved and targeted content delivery to the user and the user's household.

Abstract: Embodiments include one or more client devices accessible by users, an online system, and one or more partner systems such that the online system is able to identify a user of the online system across different devices and browsers based on the user activity that occurs external to the online system. A user performs user actions (e.g. purchase a product) on a web page of a partner system and may provide personally identifiable information (PII) to the partner system. The partner system provides the hashed PII and user actions performed by the user to the online system. The online system identifies a user profile on the online system by matching personal information in the user profile to the hashed PII. The online system generates a confidence score indicating a likelihood that the identified user of the online system is the individual that performed the external user action.

Abstract: An online system provides content items to target users who are identified to have high incremental likelihood of performing conversion actions when presented with content items. The incremental likelihood represents the difference between the response likelihood of performing conversion actions when a content item is presented to a user, and the baseline likelihood when a content item is not presented to the user. The baseline and response likelihood for a user are predicted by one or more machine-learned models. By targeting the content to users that are likely to have a high incremental likelihood, the online system provides content items to users whose conversion actions are more likely to be impacted by the presentation of content items, rather than users that may just be of interest for performing the action.

Abstract: An advertisement system measures an ad lift metric for advertisement campaigns, which indicates the increase in conversions that can be attributed to the advertisement campaign. As impression opportunities become available for users for the ad in the lift study, the advertisement system determines whether the user is in a test group or a control group. To limit bias in the lift study, rather than holding out ads from being provided to users after the ad has been selected for the user and right before the impression, the system holds out the ads at a higher level in the ad selection process. In this manner, not all test group users receive the advertisement. The system computes the lift metric as e.g., the incremental lift (difference between conversion rates in the test and control groups), and this is divided by conversion rate of an exposed target group minus the incremental lift.

Abstract: Systems, methods, and non-transitory computer-readable media can determine attribute information associated with attributes. The attribute information is associated with a first user and a second user. Match values for the attributes are determined based on the attribute information. A first rule is applied to the match values. The first user and the second user are predicted to be members in a first common household based on satisfaction of the first rule by the match values.

Abstract: A group of social networking system users are associated with a holdout group for an advertisement. Users in the holdout group are not presented with the advertisement. When the advertisement is selected for presentation to a user, the social networking system presents the advertisement to the user if the user is not in the holdout group. However, if the user is in the holdout group, alternative content is presented to the user. If a user performs a conversion event associated with the advertisement via a client device, the social networking system determines a fee for an advertiser if the advertisement was presented to the user. The fee may be adjusted based on differences between conversion events by users in the holdout group for the advertisement and by users not in the holdout group.