Abstract: A brand engine receives a request from a user device operated by a first user to display user information of a second user. User information of the first user is mapped to at least the user information of the second user by the brand engine. The mapping may map the user information of the first user to user information of further users in a social network. The mapping may map of any combination of spatial, temporal, social, and topical data related to the users. A modified representation of received content is generated by the brand engine based on the mapping. The modified representation is transmitted to the user device. The user device displays the modified representation for the first user. The modified representation of the received content may include any combination of filtered and/or sorted content items, recommended content items, and/or modified content items.

Abstract: A brand engine receives a request from a user device operated by a first user to display user information of a second user. User information of the first user is mapped to at least the user information of the second user by the brand engine. The mapping may map the user information of the first user to user information of further users in a social network. The mapping may map of any combination of spatial, temporal, social, and topical data related to the users. A modified representation of a brand profile of the second user is generated by the brand engine based on the mapping. The modified representation is transmitted to the user device. The user device displays the modified representation for the first user. The modified representation of the brand profile may include any combination of filtered and/or sorted brand objects, recommended brand objects, and/or modified brand objects.

Abstract: A system, method and computer program product is described that matches commercial incentives to consumers based on targeting data associated with the commercial incentive and user data associated with consumers in a manner that advantageously allows for highly-targeted delivery of commercial incentives to the consumers that are most likely to use them. Because both the targeting data and the user data may include spatial, temporal, social and topical data, matching may be performed based on any or all of these data types, as well as based on any combination of these data types.

Abstract: A system, method and computer program product is described that matches commercial incentives to consumers based on targeting data associated with the commercial incentive and user data associated with consumers in a manner that advantageously allows for highly-targeted delivery of commercial incentives to the consumers that are most likely to use them. Because both the targeting data and the user data may include spatial, temporal, social and topical data, matching may be performed based on any or all of these data types, as well as based on any combination of these data types.

Abstract: A system, method and computer program product is described that matches commercial incentives to consumers based on targeting data associated with the commercial incentive and user data associated with consumers in a manner that advantageously allows for highly-targeted delivery of commercial incentives to the consumers that are most likely to use them. Because both the targeting data and the user data may include spatial, temporal, social and topical data, matching may be performed based on any or all of these data types, as well as based on any combination of these data types.

Abstract: A system, method and computer program product is described that matches commercial incentives to consumers based on targeting data associated with the commercial incentive and user data associated with consumers in a manner that advantageously allows for highly-targeted delivery of commercial incentives to the consumers that are most likely to use them. Because both the targeting data and the user data may include spatial, temporal, social and topical data, matching may be performed based on any or all of these data types, as well as based on any combination of these data types.

Abstract: A commercial incentive engine and network is described that advantageously allows content and/or terms associated with a commercial incentive that has already been distributed to a consumer over a network to be dynamically augmented. The dynamic augmentation may occur responsive to one or more of revisions to the content and/or terms input by an advertiser, responsive to business information provided by an advertiser, or responsive to spatial, temporal, social and topical data associated with the consumer. An alternate implementation also advantageously allows the content and/or terms associated with a commercial incentive to be dynamically determined prior to distribution of the commercial incentive to a consumer over a network.

Abstract: A brand engine receives a request from a user device operated by a first user to display user information of a second user. User information of the first user is mapped to at least the user information of the second user by the brand engine. The mapping may map the user information of the first user to user information of further users in a social network. The mapping may map of any combination of spatial, temporal, social, and topical data related to the users. A modified representation of a brand profile of the second user is generated by the brand engine based on the mapping. The modified representation is transmitted to the user device. The user device displays the modified representation for the first user. The modified representation of the brand profile may include any combination of filtered and/or sorted brand objects, recommended brand objects, and/or modified brand objects.

Abstract: A brand engine receives a request from a user device operated by a first user to display user information of a second user. User information of the first user is mapped to at least the user information of the second user by the brand engine. The mapping may map the user information of the first user to user information of further users in a social network. The mapping may map of any combination of spatial, temporal, social, and topical data related to the users. A modified representation of received content is generated by the brand engine based on the mapping. The modified representation is transmitted to the user device. The user device displays the modified representation for the first user. The modified representation of the received content may include any combination of filtered and/or sorted content items, recommended content items, and/or modified content items.

Abstract: A commercial incentive engine and network is described that advantageously allows content and/or terms associated with a commercial incentive that has already been distributed to a consumer over a network to be dynamically augmented. The dynamic augmentation may occur responsive to one or more of revisions to the content and/or terms input by an advertiser, responsive to business information provided by an advertiser, or responsive to spatial, temporal, social and topical data associated with the consumer. An alternate implementation also advantageously allows the content and/or terms associated with a commercial incentive to be dynamically determined prior to distribution of the commercial incentive to a consumer over a network.

Abstract: Systems and methods are described for permitting commercial incentives received by a consumer device to be redistributed to other consumer devices, thereby facilitating the sharing and/or transport of commercial incentives between and among consumers. Systems and methods for distributing and/or redistributing commercial incentives from a node to consumer devices within a local network associated with the node are also described. In both distribution and redistribution scenarios, compensation may be issued to a distributor and/or redistributor upon redemption of a commercial incentive.

Abstract: A system, method and computer program product is described that matches commercial incentives to consumers based on targeting data associated with the commercial incentive and user data associated with consumers in a manner that advantageously allows for highly-targeted delivery of commercial incentives to the consumers that are most likely to use them. Because both the targeting data and the user data may include spatial, temporal, social and topical data, matching may be performed based on any or all of these data types, as well as based on any combination of these data types.