Abstract: Systems, methods, and computer media for integrating requests for reserved allocations of advertisement impressions and requests for dynamic allocations of advertisement impressions are provided. A request is received from a first advertiser to purchase a reserved allocation of advertisement impressions. The request specifies a requested number of impressions that each have one or more requested attributes. One or more bids to dynamically purchase one or more impressions through a real-time bidding process are received from an external bidding agent. An internal bidding agent bids, on behalf of the first advertiser, to dynamically purchase one or more impressions through the real-time bidding process until the request from the first advertiser to purchase the reserved allocation of impressions is satisfied.

Abstract: This document relates to meeting evaluation. One example determines previous meeting attributes of previous meetings that were attended by a user or to which the user was invited, and obtains implicit feedback about the previous meetings from a device of the user. The example includes training a predictive algorithm to evaluate future meetings for the user using the previous meeting attributes and the implicit feedback about the previous meetings.

Abstract: A recommendation ranking system that computes trust for entities based on negative expressions of trust. Negative expressions of trust are used to reduce the trust of entities. However, the system may discount entities that in the aggregate are distrusted. The system may be used with a social network to provide accurate, personalized recommendations for members of the social network. The network may be modeled as a voting network, with each member of the social network represented as a node and expressions of trust between members represented as weights on edges between nodes. Values of trust may be computed for nodes in the network and used to generate a recommendation. Opinions expressed on a topic may be weighted by trust in the node expressing the opinion. The system may be applied in other settings that can be modeled as a voting network, including ranking of Internet search results.

Abstract: Online selling mechanism creation is described, for example, whereby a retailer with business goals and constraints benefits from automatic generation and execution of software which controls generation of online offers so as to meet the business goals within the constraints. For example, as business goals and constraints change over time, bespoke selling mechanisms may be automatically updated. In various examples business goals are used to select selling mechanisms from a plurality of available online selling mechanisms; properties of the selling mechanisms may be taken into account. In examples, generic software implementing the selling mechanisms is used to instantiate a bespoke selling mechanism according to particular business goals and constraints. For example, a bespoke selling mechanism may be executed at a commerce server so as to control dynamic generation of offers.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that dynamically set floor prices in a second price auction for impressions or events. Advertiser bids for the impressions or events are received by the computer system and used to create a statistical distribution. The advertiser with the highest bid for an impression or event is identified. The statistical distribution excludes bids received from the advertiser having the highest bid. The computer system sets a floor price for the impression or event. The floor price is based on the statistical distribution. The advertiser is charged the maximum of the floor price or the second highest bid received from another advertiser for the same impression. The advertisement associated with the advertiser having the highest bid is selected for display in a webpage that corresponds to the impression or event.

Abstract: Systems, methods, and computer-readable storage media are provided for determining configurable auction parameters based upon historic auction information that includes identities of auction participants or bidders. Such configurable auction parameters specify values to be applied for particular auction parameters (e.g., floor price), at the time of an auction for available advertising space, when one or more of the auction participants are associated with specified identities. Further provided are systems and methods for, at the time of an auction for available advertising space, determining auction parameter values in accordance with configurable auction parameters.

Abstract: A recommendation system may use a network of relationships between many different entities to find search results and establish a relevance value for the search results. The relevance value may be calculated by analyzing trust and similarity components of each relationship between the search user and the entity providing the search results. The entities may be, for example, persons associated within express or implied social networks, or corporations or other organizations with a historical or other reputation. The relationships may be created through many different contact mechanisms and may be unidirectional, asymmetric bidirectional, or symmetric bidirectional relationships. The relationships may be different based on topic or other factors.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate advertisement selection using advancement bids. In embodiments, advertisement attributes in association with an advertisement are referenced. Such advertisement attributes might include an impression bid and a click bid. For a particular advertisement, it is determined if a satisfactory click rate is available. If it is determined that the satisfactory click rate is unavailable for the advertisement, an impression bid is utilized to compete with other advertisements in an advertisement-selection auction. On the other hand, if it is determined that the satisfactory click rate is available for the advertisement, the satisfactory click rate and the click bid are utilized (e.g., via an effective impression value) to compete with the other advertisements in the advertisement-selection auction.

Abstract: A social marketing system may have an incentive system that may be optimized dynamically for each user during the course of a marketing campaign. The social marketing system may use a simulated model of social interactions to predict the performance of a marketing campaign and may use the output of the simulation to adjust incentives during a campaign for various users, as well as use the actual results of changes in incentives as feedback to the simulation. The simulation may assume several different types of users within the social network and that several types of financial and non-financial incentives may be applied to different users. Some embodiments may use machine learning algorithms to analyze actual results and feed those results into the simulation. The system may be able to categorize users into the simulated types and adjust incentives according to the models associated with those types of users.

Abstract: A social marketing system may reward and incentivize participants, and may also have a fraud detection system. The manager may create social marketing campaigns that may be simulated to determine an expected set of activities, which may be compared to an actual set of activities. A fraud detection system may detect abnormal activity and may bring the activity to a manager's attention and may also punish the participants by withholding rewards, lowering the participant's reputation, or some other punishment mechanism.

Abstract: Social influencers may be identified for specific usage contexts and for influencer type. Influencers may be categorized by mavens, connectors, salesmen, or other categories. Within each usage context, a unified data model may be used to collect data from multiple sources, including multiple social networks, as well as to collect data from different levels of influencers in each usage context. The relevance of various communication media as well as the frequency and quality of use of the media may be factors used to determine a person's effectiveness as a specific type of influencer within a usage context.

Abstract: A social incentive system is described herein that formalizes information propagation through social networks in a structured and systematic way. The system provides incentives and rewards to entities who participate in propagating the information, allowing heavy influencers to gain from their influence while the marketer rewards them. The system provides one or more tools for creation and design of social incentive plans with rewards for socially distributing information, including marketing campaigns. In particular, the system introduces a semantic framework where marketers can create structured incentive plans for rewarding consumers and distribution platforms for distributing information through social networks. As users complete specified activities they earn points, and the points can be redeemed for various incentives, such as cash, debit cards, prizes, merchandise, subscriptions, and so forth. The framework is robustly designed to avoid abuse and allow for fraud detection.

Abstract: A social marketing manager may facilitate marketing campaigns in online social networks by creating and monitoring campaigns, as well as facilitating online social interactions. A campaign manager may create a campaign and define various operational parameters. A recruitment system may identify social influencers through which the campaign may be started, and a promotion manager may create and track objects that may be passed to participants in the campaign. An analysis and monitoring system may determine the overall effectiveness of the campaign and provide feedback, payments to participants, or other results of the campaign.

Abstract: Systems, methods, and computer media for integrating requests for reserved allocations of advertisement impressions and requests for dynamic allocations of advertisement impressions are provided. A request is received from a first advertiser to purchase a reserved allocation of advertisement impressions. The request specifies a requested number of impressions that each have one or more requested attributes. One or more bids to dynamically purchase one or more impressions through a real-time bidding process are received from an external bidding agent. An internal bidding agent bids, on behalf of the first advertiser, to dynamically purchase one or more impressions through the real-time bidding process until the request from the first advertiser to purchase the reserved allocation of impressions is satisfied.

Abstract: A social network marketplace may monitor communications between an advertiser and a consumer by generating signatures for communications and tracking those signatures through social network communications until a sale may be consummated. The marketplace may monitor the transactions to determine a user's influence on other users. A user's influence may also be determined or supplemented by monitoring formal or informal social interactions performed on a computer when those communications are able to be monitored. The influence information may be used to select outbound advertisements to those users who may benefit from an advertisement, as well as to filter inbound advertisements to suit a user or a user's situation.

Abstract: Providing a recommendation to a group of networked members is disclosed. The recommendation is provided to the group collectively, and is based on trust relationships between the members of the network. In an example embodiment, the network is a social network. Example systems and methods include a two-phase approach and a one-phase approach, each including analysis and aggregation of input associated with members of the network.

Abstract: A recommendation ranking system that computes trust for entities based on negative expressions of trust. Negative expressions of trust are used to reduce the trust of entities. However, the system may discount entities that in the aggregate are distrusted. The system may be used with a social network to provide accurate, personalized recommendations for members of the social network. The network may be modeled as a voting network, with each member of the social network represented as a node and expressions of trust between members represented as weights on edges between nodes. Values of trust may be computed for nodes in the network and used to generate a recommendation. Opinions expressed on a topic may be weighted by trust in the node expressing the opinion. The system may be applied in other settings that can be modeled as a voting network, including ranking of Internet search results.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate advertisement selection using advancement bids. In embodiments, advertisement attributes in association with an advertisement are referenced. Such advertisement attributes might include an impression bid and a click bid. For a particular advertisement, it is determined if a satisfactory click rate is available. If it is determined that the satisfactory click rate is unavailable for the advertisement, an impression bid is utilized to compete with other advertisements in an advertisement-selection auction. On the other hand, if it is determined that the satisfactory click rate is available for the advertisement, the satisfactory click rate and the click bid are utilized (e.g., via an effective impression value) to compete with the other advertisements in the advertisement-selection auction.

Abstract: Systems and methods that analyze aggregated item evaluation behavior of users, to suggest a recommendation for the item. An analysis component forms a collective opinion by taking as input votes of users and trusted relationships established therebetween, to output an evaluation and/or recommendation for the item. Accordingly, within a linked structure of nodes, personalized recommendations to users (e.g., agents) are supplied about an item(s) based upon the opinions/reviews of other users, and in conjunction with the declared trust between the users.

Abstract: A recommendation system may use a network of relationships between many different entities to find search results and establish a relevance value for the search results. The relevance value may be calculated by analyzing trust and similarity components of each relationship between the search user and the entity providing the search results. The entities may be, for example, persons associated within express or implied social networks, or corporations or other organizations with a historical or other reputation. The relationships may be created through many different contact mechanisms and may be unidirectional, asymmetric bidirectional, or symmetric bidirectional relationships. The relationships may be different based on topic or other factors.