Abstract: In some embodiments, a computer system generates a recommendation for a user of an online service based on user actions that have been performed by the user within a threshold amount of time before the generation of the recommendation. For each user action, the computer system determines an intent classification that identifies an activity of the user and that corresponds to different types of user actions, as well as a preference classification that identifies a target of the activity, and then stores these intent and preference classifications as part of indications of the user actions for use in generating different types of recommendations using different types of recommendation models. Additionally, the computer system may use mini-batches of data from an incoming stream of logged data to train an incremental update to one or more recommendation models.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Potential Consumer Engine identifies profile data of a target member account accessible in a social network service during a period of time equal in length to a pre-submission time period. The Potential Consumer Engine identifies channel data and assembles feature vector data for the target member account. The Potential Consumer Engine inputs into generalized linear mix model the feature vector data. The Potential Consumer Engine receives predictive output from the generalized linear mix model. The predictive output indicative of a current probability of the target member account joining the freelancer marketplace.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Potential Consumer Engine identifies profile data of a target member account accessible in a social network service during a period of time equal in length to a pre-submission time period. The Potential Consumer Engine identifies channel data and assembles feature vector data for the target member account. The Potential Consumer Engine inputs into generalized linear mix model the feature vector data. The Potential Consumer Engine receives predictive output from the generalized linear mix model. The predictive output indicative of a current probability of the target member account joining the freelancer marketplace.

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: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Classification Engine for identifying, according to encoded rules of a plurality of service models, at least one type of service offered by a target member account of a social network service. The Classification Engine classifies, according to encoded rules of a freelancer inference model, the target member account as a freelancer account. The Classification Engine sends an invitation to the freelancer account to join a freelance marketplace within the social network service. The freelance marketplace includes various consumer accounts requesting to purchase a performance various types of services and specialties.

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.

Abstract: Techniques for upselling a limited job posting to a premium job posting are described. A determination module can access job listing data from a limited job. Additionally, the determination module can access member data from a social network. Furthermore, the determination module can determine a value for the limited job posting based on the accessed job listing data and the accessed member data. Moreover, the determination module can generate a job application based on the accessed job listing data and the accessed member data, when the determined value is above a predetermined threshold. Subsequently, the determination module and an upsell module can upsell the limited job posting to a premium job posting by using the generated job application data. In some instances, the upsell module can market to the job poster in order to upsell the limited job listing, and fill empty job slots already paid by the job poster.