Abstract: The technical problem of identifying relevant social proof information with respect to a premium service for a given member profile in an online connection network system is addressed by, first, capturing the associated member's intent based on the member's activity on the web site provided by the online connection network system. The determined intent is used as input into a relevance machine learning model that is executed to identify the member's connection who is a subscriber to the premium service and who has been identified as the most convincing resonated connection of the member with respect to subscribing to the premium service.

Abstract: The technical problem of identifying relevant social proof information with respect to a premium service for a given member profile in an online connection network system is addressed by, first, capturing the associated member's intent based on the member's activity on the web site provided by the online connection network system. The determined intent is used as input into a relevance machine learning model that is executed to identify the member's connection who is a subscriber to the premium service and who has been identified as the most convincing resonated connection of the member with respect to subscribing to the premium service.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system identifies a set of providers that meet a set of requirements in a request for proposal (RFP) from a consumer based on an overall compatibility between the set of requirements and the set of providers. Next, the system generates a ranking of the set of providers based on the compatibility and a network distance between the consumer and the set of providers in a social network. The system then selects one or more providers from the ranking as matches for the RFP. Finally, the system transmits the RFP to the one or more providers.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Prediction Engine for identifying service provider account(s) in a social network service based in part on request data representative of a request, from a target consumer account in the social network service, for a service. The Prediction Engine assembles, according to encoded rules of a prediction model, feature vector data for each identified service provider account, wherein each encoded rule of the prediction model comprises a pre-defined featurer(s) associated with a learned coefficient representing an importance of the respective pre-defined feature. The Prediction Engine generates, based on the feature vector data and the encoded rules of the prediction model, prediction output for each identified service provider account. The prediction output indicative of a likelihood that a respective service provider account will perform an action related to the requested service.

Abstract: In various example embodiments, a system and method for generating and ranking service provider recommendations in a social network are presented. A request to identify a set of service providers that meet a specific criteria is received. The request can be associated with a member of a social network service. Member profiles from among a plurality of member profiles of the social network service are identified based on the specific criteria. A plurality of attributes is extracted from the identified member profiles. A trust factor for each identified member profile indicating the likelihood that the member would trust the service provider is determined based on the extracted profile attributes. The set of service providers is ranked based on the determined trust factor. At least a portion of the set of ranked service providers may be caused to be presented to the member.