Abstract: Computer-implemented methods and systems are provided that generate and store data representing signals and recommendations between account holders of a professional social network. Each signal identifies interest of a first account holder in a second account holder. Characteristic signal weights and total recommendation weights can be calculated and stored for the account holders. Signal weight data of a given account holder can be displayed as part of the profile of the given account holder. Characteristic signal weights of a plurality of account holders that match a job opening or opportunity can be used to rank or filter the plurality of account holders in referring the plurality of account holders to a party associated with the job opening. Characteristic signal weights associated with a plurality of job openings or opportunities can be used to rank or filter the associated job openings or opportunities in notifying at least one account holder.

Abstract: A computer-implemented method and system is provided that involves storing job-descriptor data for each job opening of a plurality of job openings. The job-descriptor data for a given job opening can represent words or text phrases related to the given job opening. The job-descriptor data for each job opening is processed to derive and store words or root forms or synonyms corresponding to the job descriptor data of each job opening of the plurality of job openings. A user interacts with the system to review at least one job opening belonging to the plurality of job openings, which involves the user classifying at least one word or text phrase of the job-descriptor data for the at least one job opening. The at least one word or text phrase classified by the user is processed to derive and store words or root forms or synonym corresponding to the at least one word or text phrase classified by the user.

Abstract: A computer-implemented method and system is provided that involves a plurality of users of a social network. The method and system are configured to interact with the plurality of users to generate recommendations, wherein each recommendation is made by a first user and recommends a second user, and store data representing the recommendations. Total recommendation weights corresponding to the plurality of users as well as recommendation weights corresponding to the recommendations are dynamically calculated, and data representing the total recommendation weights corresponding to the plurality of users and data representing the recommendation weights corresponding to the recommendations is stored in data storage. Data representing the total recommendation weight corresponding to a given user can be displayed in conjunction with the display of at least part of a profile of the given user.