LinkedIn as a professional network serves the career needs of 450 Million plus members. The task of job recommendation system is to nd the suitable job among a corpus of several million jobs and serve this in real time under tight latency constraints. Job search involves nding suitable job listings given a user, query and context. Typical scoring function for both search and recommendations involves evaluating a function that matches various elds in the job description with various elds in the member pro le. This in turn translates to evaluating a function with several thousands of features to get the right ranking. In recommendations, evaluating all the jobs in the corpus for all members is not possible given the latency constraints. On the other hand, reducing the candidate set could potentially involve loss of relevant jobs. We present a way to model the underlying complex ranking function via decision trees. The branches within the decision trees are query clauses and hence the decision trees can be mapped on to real time queries. We developed an o ine framework which evaluates the quality of the decision tree with respect to latency and recall. We tested the approach on job search and recommendations on LinkedIn and A/B tests show signi cant improvements in member engagement and latency. Our techniques helped reduce job search latency by over 67% and our recommendations latency by over 55%. Our techniques show 3.5% improvement in applications from job recommendations primarily due to reduced timeouts from upstream services. As of writing the approach powers all of job search and recommendations on LinkedIn.

The mission of LinkedIn is to connect the world's professionals to make them more productive and successful. LinkedIn operates the world's largest professional network on the Internet with more than 500 Million members in over 200 countries. Core to realizing the mission is to help people find jobs. In this paper, we describe how the jobs recommendations is powered by a search index and some practical challenges involved in scaling such a system.

Modern day social media search and recommender systems require complex query formulation that incorporates both user context and their explicit search queries. Users expect these systems to be fast and provide relevant results to their query and context. With millions of documents to choose from, these systems utilize a multi-pass scoring function to narrow the results and provide the most relevant ones to users. Candidate selection is required to sift through all the documents in the index and select a relevant few to be ranked by subsequent scoring functions. It becomes crucial to narrow down the document set while maintaining relevant ones in resulting set. In this tutorial we survey various candidate selection techniques and deep dive into case studies on a large scale social media platform. In the later half we provide hands-on tutorial where we explore building these candidate selection models on a real world dataset and see how to balance the tradeoff between relevance and latency.

Job Search is a core product at LinkedIn which makes it essential to generate highly relevant search results when a user searches for jobs on Linkedin. Historically job results were ranked using linear models consisting of a combination of user, job and query features. This paper talks about a new generalized mixed effect models introduced in the context of ranking candidate job results for a job search performed on LinkedIn. We build a per-query model which is populated with coefficients corresponding to job-features in addition to the existing global model features. We describe the details of the new method along with the challenges faced in launching such a model into production and making it efficient at a very large scale. Our experiments show improvement over previous baseline ranking models, in terms of offline metrics (both AUC and [email protected] metrics) as well as online metrics in production (Job Applies) which are of interest to us. The resulting method is more powerful and has also been adopted in other applications at LinkedIn successfully.

This paper proposes an approach to applying standardized entity data to improve job search quality and to make search results more personalized. Specifically, we explore three types of entity-aware features and incorporate them into the job search ranking function. The first is query-job matching features which extract and standardize entities mentioned in queries and documents, then semantically match them based on these entities. The second type, searcher-job expertise homophily, aims to capture the fact that job searchers tend to be interested in the jobs requiring similar expertise as theirs. To measure the similarity, we use standardized skills in job descriptions and searchers' profiles as well as skills that we infer searchers might have but not explicitly list in their profiles. Third, we propose a concept of entity-faceted historical click-through-rates (CTRs) to capture job document quality. Faceting jobs by their standardized companies, titles, locations, etc., and computing historical CTRs at the facet level instead of individual job level alleviate sparseness issue in historical action data. This is particularly important in job search where job lifetime is typically short. Both offline and online experiments confirm the effectiveness of the features. In offline experiment, using the entity-aware features gives improvements of +20%, +12.1% and +8.3% on [email protected], MRR and [email protected], respectively. Online A/B test shows that a new model with these features is +11.3% and +5.3% better than the baseline in terms of click-through-rate and apply rate.

Instant search has become a common part of the search experience in most popular search engines and social networking websites. The goal is to provide instant feedback to the user in terms of query completions ("instant suggestions") or directly provide search results ("instant results") as the user is typing their query. The need for instant search has been further amplified by the proliferation of mobile devices and services like Siri and Google Now that aim to address the user's information need as quickly as possible. Examples of instant results include web queries like "weather san jose" (which directly provides the current temperature), social network queries like searching for someone's name on Facebook or LinkedIn (which directly provide the people matching the query). In each of these cases, instant search constitutes a superior user experience, as opposed to making the user complete their query before the system returns a list of results on the traditional search engine results page (SERP).

LinkedIn has grown to become a platform hosting diverse sources of information ranging from member profiles, jobs, professional groups, slideshows etc. Given the existence of multiple sources, when a member issues a query like "software engineer", the member could look for software engineer profiles, jobs or professional groups. To tackle this problem, we exploit a data-driven approach that extracts searcher intents from their profile data and recent activities at a large scale. The intents such as job seeking, hiring, content consuming are used to construct features to personalize federated search experience. We tested the approach on the LinkedIn homepage and A/B tests show significant improvements in member engagement. As of writing this paper, the approach powers all of federated search on LinkedIn homepage.