Many social networking services, such as Facebook or the professional social networking service LinkedIn®, make recommendations to their users. These recommendations may pertain to people with whom to connect, articles to read, jobs for which to apply, etc. The quality and relevance of such recommendations may be heavily dependent on the underlying representation of various content items used to generate such recommendations. Examples of content items or objects are a member profile, a job posting, a SlideShare article, a Pulse article, etc.
Today, the quality of many recommendations suffers from the problem of vocabulary mismatch between different content types. For example, if a member profile of a member of a social networking service (also referred to herein as “SNS”) and a job description use different terminologies to refer to the same underlying concept, the SNS may fail to match the member profile to the job description, and to recommend the respective job to the member.
To address this problem, it may be beneficial to an SNS to generate a universal concept graph that includes a unified and standardized set of concept phrases that may be used to generate better recommendations to the members of the SNS.