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

Data-driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.Comment: 8 pages, 10 figures, ICDM 201

Building a recommendation system based on the job offers extracted from the web and the skills of job seekers

Recruitment, or job search, is increasingly used throughout the world by a large population of users through various channels, such as websites, platforms, and professional networks. Given the large volume of information related to job descriptions and user profiles, it is complicated to appropriately match a user's profile with a job description, and vice versa. The job search approach has drawbacks since the job seeker needs to search a job offers in each recruitment platform, manage their accounts, and apply for the relevant job vacancies, which wastes considerable time and effort. The contribution of this research work is the construction of a recommendation system based on the job offers extracted from the web and on the e-portfolios of job seekers. After the extraction of the data, natural language processing is applied to structured data and is ready for filtering and analysis. The proposed system is a content-based system, it measures the degree of correspondence between the attributes of the e-portfolio with those of each job offer of the same list of competence specialties using the Euclidean distance, the result is classified with a decreasing way to display the most relevant to the least relevant job offers

Landing on the right job : a machine learning approach to match candidates with jobs applying semantic embeddings

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsJob application’ screening is a challenging and time-consuming task to execute manually. For recruiting companies such as Landing.Jobs it poses constraints on the ability to scale the business. Some systems have been built for assisting recruiters screening applications but they tend to overlook the challenges related with natural language. On the other side, most people nowadays specially in the IT-sector use the Internet to look for jobs, however, given the huge amount of job postings online, it can be complicated for a candidate to short-list the right ones for applying to. In this work we test a collection of Machine Learning algorithms and through the usage of cross-validation we calibrate the most important hyper-parameters of each algorithm. The learning algorithms attempt to learn what makes a successful match between candidate profile and job requirements using for training historical data of selected/reject applications in the screening phase. The features we use for building our models include the similarities between the job requirements and the candidate profile in dimensions such as skills, profession, location and a set of job features which intend to capture the experience level, salary expectations, among others. In a first set of experiments, our best results emerge from the application of the Multilayer Perceptron algorithm (also known as Feed-Forward Neural Networks). After this, we improve the skills-matching feature by applying techniques for semantically embedding required/offered skills in order to tackle problems such as synonyms and typos which artificially degrade the similarity between job profile and candidate profile and degrade the overall quality of the results. Through the usage of word2vec algorithm for embedding skills and Multilayer Perceptron to learn the overall matching we obtain our best results. We believe our results could be even further improved by extending the idea of semantic embedding to other features and by finding candidates with similar job preferences with the target candidate and building upon that a richer presentation of the candidate profile. We consider that the final model we present in this work can be deployed in production as a first-level tool for doing the heavy-lifting of screening all applications, then passing the top N matches for manual inspection. Also, the results of our model can be used to complement any recommendation system in place by simply running the model encoding the profile of all candidates in the database upon any new job opening and recommend the jobs to the candidates which yield higher matching probability

News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs

Analyzing Business-Focused Social Networks in Hiring: The Influence of a Job Candidate\u27s Network on a Recruiter\u27s Hiring Recommendation

Social media has altered the ways in which people interact. Business-focused social media profiles, such as those on LinkedIn, can act as a proxy for a traditional resume. However, these websites differ from a traditional resume in that information presented is sometimes informal, personal, and irrelevant to the member’s career. Furthermore, HR employees are able to view a job candidate’s social network. This research investigates the influence of a recruiter’s knowledge of an applicant’s professional network on the recruiter’s perception of the applicant’s trustworthiness and hence their willingness to take risk in the hiring relationship. A review of the literature covered two areas of research: trust and the use of social networks in hiring. While previous studies connected the trust model to LinkedIn, none of them addressed the influence of a LinkedIn profile’s social network on a hiring manager’s perception of the candidate’s trustworthiness. A survey-based experiment was designed to evaluate how network association bias, a newly created construct, affects a hiring manager’s perception of a job candidate’s ability and benevolence. The experimental model was based on Mayer, Davis, and Schoorman’s trust model. A structural equation modeling (SEM) analysis was conducted in RStudio using the lavaan latent variable modeling package. ix The results of this experiment reveal that that a job candidate’s social network impacts how the candidate’s levels of ability and benevolence are perceived by others. Furthermore, it is suggested that a recruiter’s propensity to trust influences the relationship between network association bias and a job candidate’s ability