Abstract: Technologies for using an inferred relationship between data sets to computationally align the data sets and generate actionable output include obtaining, from an online system, a set of member records; inferring a job category based at least partly on job function data extracted from the set of member records; obtaining, from the management system, a set of job records that correspond to the inferred job category; calculating an aggregate alignment score by comparing member skills data extracted from the set of member records to job skills data extracted from the set of job records, where the aggregate alignment score is a measure of a mismatch between the member skills data for the set of member records and the job skills data for the set of job records; based on the aggregate alignment score, identifying a skill that is actionable to address the mismatch between the member skills data and the job skills data; generating actionable output that replaces, modifies, or supplements first course recommendation d

Abstract: Techniques for generating customized learning paths are provided. In one technique, consumption data that indicates consumption of multiple learning resources by multiple users is recorded. Based on the consumption data, multiple learning resource tuples are generated, each learning resource tuple indicating that one learning resource that was consumed by a user prior to another learning resource that was consumed by the user. Multiple aggregations are performed, where each aggregation involves aggregating different sets of learning resource tuples, where each set of learning resource tuples comprises the same two learning resources in the same order. Based on a subset of the aggregations, a customized learning path that comprises a set of learning resources is generated for a particular user. The customized learning path is presented to the particular user.

Abstract: Techniques for generating customized learning paths are provided. In one technique, consumption data that indicates consumption of multiple learning resources by multiple users is recorded. Based on the consumption data, multiple learning resource tuples are generated, each learning resource tuple indicating that one learning resource that was consumed by a user prior to another learning resource that was consumed by the user. Multiple aggregations are performed, where each aggregation involves aggregating different sets of learning resource tuples, where each set of learning resource tuples comprises the same two learning resources in the same order. Based on a subset of the aggregations, a customized learning path that comprises a set of learning resources is generated for a particular user. The customized learning path is presented to the particular user.