In an example embodiment, a confidential data architecture is extended by providing components for reliably inferring confidential data insights (e.g., median salary) for cohorts with little or no actual submitted confidential data. This is performed by inferring confidential data values based on organizations that are considered to be peers to the organization of interest. This solution involves two parts: the generation of a novel, semantic representation (embedding) of organizations to be used to compute a similarity measure between any two organizations, and the use of the semantic representation to compute a peer organization group of a given company.

Generating semantic representations for organizations and computing peer organization groups

Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system determines a set of candidate alternate search terms based on historical search logs that include records of previously submitted search terms, corresponding search results that were presented to users, and corresponding search results that were selected by the users. The set of candidate alternate search terms is selected from titles of the corresponding search results that were selected by the users. The search system ranks the set of candidate alternate search terms based on determined probabilities that each of the alternate candidate search terms will be selected if presented to a user, and selects a first candidate alternate search term from the set of candidate alternate search terms based on the ranking. The search system generates an expanded search term based on the first candidate alternate search term.

Expanding search queries

In an example embodiment, submitted confidential data of a certain cohort (e.g., title, region, organization) is augmented by modeling confidential data of a more generalized cohort based on peer organizations. The modeling may be performed using Bayesian modeling and the results used to infer confidential data values for the original cohort. The inferred confidential data values can then be used to generate statistical insights for display in a graphical user interface.

Bayesian smoothing of confidential data values at organization level using peer organization group

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Bayesian smoothing of confidential data values at organization level using peer organization group