From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach

One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position. To improve search efficiency, we propose the next generation of talent search at LinkedIn, also referred to as Search By Ideal Candidates. In this system, a searcher provides one or several ideal candidates as the input to hire for a given position. The system then generates a query based on the ideal candidates and uses it to retrieve and rank results. Shifting from the traditional Query-By-Keyword to this new Query-By-Example system poses a number of challenges: How to generate a query that best describes the candidates? When moving to a completely different paradigm, how does one leverage previous product logs to learn ranking models and/or evaluate the new system with no existing usage logs? Finally, given the different nature between the two search paradigms, the ranking features typically used for Query-By-Keyword systems might not be optimal for Query-By-Example. This paper describes our approach to solving these challenges. We present experimental results confirming the effectiveness of the proposed solution, particularly on query building and search ranking tasks. As of writing this paper, the new system has been available to all LinkedIn members

Career Transitions and Trajectories: A Case Study in Computing

From artificial intelligence to network security to hardware design, it is well-known that computing research drives many important technological and societal advancements. However, less is known about the long-term career paths of the people behind these innovations. What do their careers reveal about the evolution of computing research? Which institutions were and are the most important in this field, and for what reasons? Can insights into computing career trajectories help predict employer retention? In this paper we analyze several decades of post-PhD computing careers using a large new dataset rich with professional information, and propose a versatile career network model, R^3, that captures temporal career dynamics. With R^3 we track important organizations in computing research history, analyze career movement between industry, academia, and government, and build a powerful predictive model for individual career transitions. Our study, the first of its kind, is a starting point for understanding computing research careers, and may inform employer recruitment and retention mechanisms at a time when the demand for specialized computational expertise far exceeds supply.Comment: To appear in KDD 201

Profiling Essential Professional Skills of Chief Data Officers Through Topical Modeling Algorithms

Today enterprises are increasingly dependent on data to keep their business competitive and successful. To better harness values of data, more and more organizations are establishing Chief Data Officer (CDO) position. The professional skills of CDOs are rather diverse because CDOs are expected to undertake a variety of roles in their companies including enterprise data architect, data quality and governance manager, business strategy leader, business regulation compliance officer, etc. CDO is an emerging research field, few studies have been done on CDO. This paper tries to profile what are the key professional skills and education background that current CDOs have by studying their resumes on LinkedIn using topic modeling technique. This work is a step forward towards understanding the roles of CDOs in organizations and what are the professional skills and experience they may need have in order to undertake their responsibilities of managing data and realizing its true values for their organizations

Talent flow analytics in online professional network

Singapore National Research Foundation under International Research Centres in Singapore Funding Initiativ

Talent Flow Analytics in Online Professional Network

Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are conducted on job seekers and employers to study job hop behavior. Beyond surveys, job hop behavior can also be studied in a highly scalable and timely manner using a data driven approach in response to fast-changing job landscape. Fortunately, the advent of online professional networks (OPNs) has made it possible to perform a large-scale analysis of talent flow. In this paper, we present a new data analytics framework to analyze the talent flow patterns of close to 1 million working professionals from three different countries/regions using their publicly-accessible profiles in an established OPN. As OPN data are originally generated for professional networking applications, our proposed framework re-purposes the same data for a different analytics task. Prior to performing job hop analysis, we devise a job title normalization procedure to mitigate the amount of noise in the OPN data. We then devise several metrics to measure the amount of work experience required to take up a job, to determine that existence duration of the job (also known as the job age), and the correlation between the above metric and propensity of hopping. We also study how job hop behavior is related to job promotion/demotion. Lastly, we perform connectivity analysis at job and organization levels to derive insights on talent flow as well as job and organizational competitiveness.Comment: arXiv admin note: extension of arXiv:1711.05887, Data Science and Engineering, 201