Abstract: An online system receives third party user identifying information. The online system accesses accuracy measures associated with each of a plurality of the user identifying information sets. The online system identifies high accuracy sets of user identifying information that include the one or more types of user identifying information included in the received types of the third party user identifying information. The online system identifies as high confidence matches those local users of the online system having the high accuracy sets of user identifying information that match a corresponding set of third party user identifying information for the plurality of third party users. The online system also identifies as low confidence matches, and identifies as similar matches those of the low confidence matches that have a measure of similarity to one or more of the high confidence matches that is beyond a threshold measure of similarity.

Abstract: In one embodiment, a method includes receiving multiple data items respectively associated with multiple conversion events and identifying, from one or more data stores associated with a social-networking system, a content object corresponding to a business location, the content object including information describing the business location. The method also includes identifying one or more users, each being associated with at least one of the conversion events and updating the content object based at least in part on a determined location of each of one or more of the identified users at a time of the conversion event associated with the user.

Abstract: An online system receives information describing actions performed by individuals and identifies online system users included among these individuals. Based on the actions they have performed, the users are assigned to sequentially ordered stages of a classification scheme associated with a content-providing user of the online system. The online system receives content items associated with different stages from the content-providing user, in which content items associated with a stage may be presented to users assigned to the stage. The online system may determine an expected return value associated with each stage and an expected advancement value associated with advancement of users assigned to each stage to succeeding stages of the classification scheme. The online system also may determine a value associated with a content item based on a comprehensive value received from the content-providing user, the expected advancement value, and a predicted likelihood of user advancement to a succeeding stage.

Abstract: In one embodiment, the system may identify a virtual network, the virtual network including a plurality of virtual entities and connections among the plurality of virtual entities. The system may automatically map each of the plurality of virtual entities to one or more resources or resource pools such that the virtual network is mapped to a physical network, wherein mapping includes allocating one or more resources or resource pools to a corresponding one of the plurality of virtual entities.

Abstract: A social networking system selects advertisements for a user based on user characteristics of the user in response to a request to present an advertisement to the user. To increase the number of advertisements eligible for presentation to the user, the social networking system associates the user with one or more cluster groups associated with targeting criteria that are not satisfied by the user's characteristics. To determine whether to associate a user with a cluster group, the social networking system determines a cluster score for the cluster group based on the user's characteristics. If the cluster score equals or exceeds a cluster cutoff score for the cluster group, the user is associated with the cluster group. The cluster cutoff score may be determined based on an estimated distribution of users so that a target number or percentage of users have cluster scores less than the cluster cutoff score.

Abstract: An online system receives third party user identifying information. The online system accesses accuracy measures associated with each of a plurality of the user identifying information sets. The online system identifies high accuracy sets of user identifying information that include the one or more types of user identifying information included in the received types of the third party user identifying information. The online system identifies as high confidence matches those local users of the online system having the high accuracy sets of user identifying information that match a corresponding set of third party user identifying information for the plurality of third party users. The online system also identifies as low confidence matches, and identifies as similar matches those of the low confidence matches that have a measure of similarity to one or more of the high confidence matches that is beyond a threshold measure of similarity.

Abstract: An online system receives information describing actions performed by individuals and identifies online system users included among these individuals. Based on the actions they have performed, the users are assigned to sequentially ordered stages of a classification scheme associated with a content-providing user of the online system. The online system receives content items associated with different stages from the content-providing user, in which content items associated with a stage may be presented to users assigned to the stage. The online system may determine an expected return value associated with each stage and an expected advancement value associated with advancement of users assigned to each stage to succeeding stages of the classification scheme. The online system also may determine a value associated with a content item based on a comprehensive value received from the content-providing user, the expected advancement value, and a predicted likelihood of user advancement to a succeeding stage.

Abstract: In one embodiment, the system may identify a virtual network, the virtual network including a plurality of virtual entities and connections among the plurality of virtual entities. The system may automatically map each of the plurality of virtual entities to one or more resources or resource pools such that the virtual network is mapped to a physical network, wherein mapping includes allocating one or more resources or resource pools to a corresponding one of the plurality of virtual entities.

Abstract: An online system receives third party user identifying information. The online system accesses accuracy measures associated with each of a plurality of the user identifying information sets. The online system identifies high accuracy sets of user identifying information that include the one or more types of user identifying information included in the received types of the third party user identifying information. The online system identifies as high confidence matches those local users of the online system having the high accuracy sets of user identifying information that match a corresponding set of third party user identifying information for the plurality of third party users. The online system also identifies as low confidence matches, and identifies as similar matches those of the low confidence matches that have a measure of similarity to one or more of the high confidence matches that is beyond a threshold measure of similarity.

Abstract: An online system determines the score for each additional user based on the measure of similarity between the additional user and a group of seed users. The online system divides the additional users into one or more segments according to their respective scores, and assigns a bid amount for each segment. The online system presents sponsored content to the additional users according to the corresponding bid amounts, and for each of the additional users in each segment that is presented with the sponsored content, the online system identifies a value generated by the additional user due to being presented with the sponsored content. The online system uses the identified values of the additional users for each segment to determine an updated configuration of assigned bid amounts for the segments that is predicted to increase a return on investment and assigns the updated bid amounts for each segment.

Abstract: In one embodiment, the system may identify a virtual network, the virtual network including a plurality of virtual entities and connections among the plurality of virtual entities. The system may automatically map each of the plurality of virtual entities to one or more resources or resource pools such that the virtual network is mapped to a physical network, wherein mapping includes allocating one or more resources or resource pools to a corresponding one of the plurality of virtual entities.

Abstract: An online system receives an advertisement request (“ad request”) including an advertisement, targeting criteria identifying characteristics of users eligible to be presented with the advertisement, and one more rules associating weights with characteristics of users. Based on the rules included in the ad request, the online system generates a cluster model that is applied to characteristics of users who do not have characteristics satisfying the targeting criteria in the ad request to generate cluster scores. Users with cluster scores equaling or exceeding a cluster group cutoff score are identified as eligible to be presented with the advertisement in the ad request despite not having characteristics satisfying the targeting criteria in the ad request. Hence, the ad request is eligible for presentation to users having characteristics satisfying the ad request's targeting criteria or having cluster scores equaling or exceeding the cluster group cutoff score.

Abstract: An online advertising system evaluates advertising opportunities for online advertising publishers. The online advertising system tracks online users via various tracking methods to receive advertising data and user information for the online users. The online advertising system identifies and segments the online users based on segmenting criteria that are associated with some interest topics (e.g., demographical information). The system calculates projected advertising revenue for each audience segment and generates an inventory optimization dashboard based on the calculated revenue. The inventory optimization dashboard helps the advertising publishers better understand the online advertising traffic and better optimize their advertising inventory. For example, the advertising publishers may advertise to specific audience segments which tend to purchase the advertised products or services.

Abstract: A system, medium and method of automatically discovering a wide area network optimized route is disclosed. A client request is received at a second optimization device to access a server. The second optimization device is of a second local area network with respect to a wide area network (WAN) and is configured to communicate with the server. A probe request is received at the second optimization device from a first optimization device of a first local area network. The probe request establishes an optimization route with the first optimization device. A probe response is sent to the first optimization device, wherein the probe response provides identifying information of the second optimization device. A paired relationship is established, wherein configuration information of the first and second optimization devices are exchanged. An optimization route based on the configuration information is exchanged between the paired first and second optimization devices.

Abstract: An online system receives information from an entity identifying a set of users of the online system and groups users included in the set into clusters based on their similarities using a clustering model or algorithm (e.g., k-means clustering) and based on one or more parameters specified by the entity. The online system generates expanded clusters that include additional users in one or more clusters based on similarities between the additional users and users in various clusters. If an additional user is included in multiple expanded clusters, the online assigns the additional user exclusively to an expanded cluster that best fits the user.

Abstract: A target audience for an ad campaign is determined during an exploration period of the ad campaign by modifying the target audience based on the fulfillment of performance objectives. An initial target audience may be provided by the advertiser or determined by the social networking system based on ad campaigns having similar ad content or other similar characteristics. Advertisements associated with the ad campaign are served to users of the initial target audience. A subset of the target audience that fulfills the performance objectives of the ad campaign is identified and those users are used to generate a new targeting audience to target users that “look like” the subset of the target audience. The new targeting audience is used in place of the initial target audience to improve targeting for the advertisement. This process may be iteratively performed to refine the target audience during the exploration period.

Abstract: A social networking system receives an advertisement request including multiple sets of targeting criteria. To increase the number of users eligible to be presented with the advertisement request, the social networking system generates a cluster group associated with each set of targeting criteria. A cluster group associated with a set of targeting criteria includes users satisfying the targeting criteria and additional users that do not satisfy the targeting criteria. The social networking system determines an amount of overlap between the cluster groups. If the amount of overlap equals or exceeds a threshold value, the social networking system combines the cluster groups to generate an overall group associated with the advertisement request.

Abstract: A social networking system selects advertisements for a user based on user characteristics of the user in response to a request to present an advertisement to the user. To increase the number of advertisements eligible for presentation to the user, the social networking system associates the user with one or more cluster groups associated with targeting criteria that are not satisfied by the user's characteristics. To determine whether to associate a user with a cluster group, the social networking system determines a cluster score for the cluster group based on the user's characteristics. If the cluster score equals or exceeds a cluster cutoff score for the cluster group, the user is associated with the cluster group. The cluster cutoff score may be determined based on an estimated distribution of users so that a target number or percentage of users have cluster scores less than the cluster cutoff score.

Abstract: In one embodiment, the system may identify a virtual network, the virtual network including a plurality of virtual entities and connections among the plurality of virtual entities. The system may automatically map each of the plurality of virtual entities to one or more resources or resource pools such that the virtual network is mapped to a physical network, wherein mapping includes allocating one or more resources or resource pools to a corresponding one of the plurality of virtual entities.

Abstract: In one embodiment, the system may identify a virtual network, the virtual network including a plurality of virtual entities and connections among the plurality of virtual entities. The system may automatically map each of the plurality of virtual entities to one or more resources or resource pools such that the virtual network is mapped to a physical network, wherein mapping includes allocating one or more resources or resource pools to a corresponding one of the plurality of virtual entities.