A third-party service may be used to assist entities in responding to requests of users. A third-party service may receive, directly or indirectly, a request of a first user for assistance from a first entity. The third-party service may request information about the first user by sending a request to a computer of the first entity. The third-party service may use the request of the first user and the information about the first user to automatically generate a response to the request of the first user. The third-party service may then transmit, directly or indirectly, the response to the first user.

A third-party company may assist other companies in providing customer support to their customers. The third-party company may provide software to a computer of a customer service representative to present a user interface to assist the customer service representative in responding to customer requests. Third-party company may also send update data to the computer of the customer service representative to cause a portion of the user interface to be updated, where the update data is determined using an intent of a message received from a customer. A message received from the customer may be processed to determine the intent of the message, a template may be obtained using the intent, and the update data may be generated by rendering the selected template. The update data may then be transmitted to the computer of the customer service representative to cause a portion of the user interface to be updated.

Disclosed apparatus, systems and methods may provide a virtual marketplace. A method performed by a manager of the marketplace includes establishing a profile on a profiler for a first device that participates in a marketplace, receiving a first message that a loan request for a resource from a computing device operated by a second entity, responsive to the loan request, identifying the first entity as a candidate for providing the resource based on the profile of the first entity when the first entity has no prior history of loaning the resource, and enabling the second entity to negotiate with the first entity for a loan of the resource. The profile may characterize one or more business activities of the first entity based on one or more transactions in the marketplace.

An amount of time needed to fill a job requirement is forecasted. By executing a forecasting algorithm, a numerosity of resumes matching the job requirement during the amount of time is forecasted. Using the numerosity and the amount of time, a risk value is computed corresponding to the job requirement, the risk value being indicative of a probability that the job requirement will go unfulfilled in the amount of time. From a base tuple corresponding to the job requirement, a second tuple is constructed, the second tuple having a distance from the base tuple. In real-time a second risk value is computed corresponding to the second tuple. When the second risk value is less than the risk value, data of the second tuple is presented as a risk minimization option for the job requirement.

In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.

In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.

Machine learning systems for predictive targeting and optimizing engagement are described herein. In various embodiments, the system includes 1) training a first machine learning computer model to generate machine predicted outcomes; (2) determining weights based on the machine predicted outcomes; (3) generating a second machine learning computer model based on the weights; and (4) generating machine learned predictions for candidates.

Machine learning systems for predictive targeting and optimizing engagement are described herein. In various embodiments, the system includes 1) training a first machine learning computer model to generate machine predicted outcomes; (2) determining weights based on the machine predicted outcomes; (3) generating a second machine learning computer model based on the weights; and (4) generating machine learned predictions for candidates.

Disclosed are systems and methods for performing efficient job applicant screening. In particular, a network-based system is established for gathering applicant information from an applicant remotely. Analysis of the received information is performed and evaluated pursuant to a first level of screening that can be done without excessive use of resources. Upon passing the first level screening, background checks and/or interviews, which require substantial resources, are further conducted. Applicants who pass the first level of screening are scheduled for interviews, including the optional initiation of personal interviews via video chat.

Aspects relate to apparatuses and methods for determining and processing dormant data records on an immutable sequential listing. An exemplary apparatus includes a processor configured to monitor a plurality of timestamps associated with a plurality of data records stored on the immutable sequential listing, where the data record includes a job resume, detect inactivity in a first data record of the plurality of data records over a predetermined time period as a function of a first timestamp of the first data record, wherein the predetermined time period may be set by the user, tag, as a function of the inactivity, the first data record as an inactive first data record, and process, as a function inactivity, the first data record, wherein processing may include adding additional data or archiving inactive data records from the immutable sequential listing.