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

Entity Personalized Talent Search Models with Tree Interaction Features

Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models which lack preference personalization in the user-level due to being trained only with globally collected recruiter activity data. In this paper, we propose an entity-personalized Talent Search model which utilizes a combination of generalized linear mixed (GLMix) models and gradient boosted decision tree (GBDT) models, and provides personalized talent recommendations using nonlinear tree interaction features generated by the GBDT. We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems. Finally, we provide offline and online experiment results benchmarking our entity-personalized model with tree interaction features, which demonstrate significant improvements in our precision metrics compared to globally trained non-personalized models.Comment: This paper has been accepted for publication at ACM WWW 201

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Pro-Resume: The Infographic Resume Builder

Scoring an interview is a challenge for any job seeker these days, thus having a unique and well-organized resume is crucial to grab a recruiter’s attention. Online resume builders such as ResumeNow and VisualizeMe have been created to help users build resumes; however, their templates are lacking in quantity, customizability, and in some instances, even legibility. Thus, our team set out to create an infographic online resume builder, a web application that allows its users to build, organize, and beautify their resumes to aid them in their job search. Our system allows for easy integration with their LinkedIn profiles so that their work history can be easily duplicated without typing everything out. There is also a large scope of infographic template options that users can choose from and, most importantly, users will have the ability to further customize their content and organization by using the system’s editing mode

Just in Time: The Beyond-the-Hype Potential of E-Learning

Based on a year of conversations with more than 100 leading thinkers, practitioners, and entrepreneurs, this report explores the state of e-learning and the potential it offers across all sectors of our economy -- far beyond the confines of formal education. Whether you're a leader, worker in the trenches, or just a curious learner, imagine being able to access exactly what you need, when you need it, in a format that's quick and easy to digest and apply. Much of this is now possible and within the next decade, just-in-time learning will likely become pervasive.This report aims to inspire you to consider how e-learning could change the way you, your staff, and the people you serve transfer knowledge and adapt over time