We Know Where You Should Work Next Summer: Job Recommendations

Abstract

Business-oriented social networks like LinkedIn or XING support people in discovering career opportunities. In this talk, we will focus on the problem of recommending job offers to Millions of XING users. We will discuss challenges of building a job recommendation system that has to satisfy the demands of both job seekers who have certain wishes concerning their next career step and recruiters who aim to hire the most appropriate candidate for a job. Based on insights gained from a large-scale analysis of usage data and profile data such as curriculum vitae, we will study features of the recommendation algorithms that aim to solve the problem. Job advertisements typically describe the job role that the candidate will need to fill, required skills, the expected educational background that candidates should have and the company and environment in which candidates will be working. Users of professional social networks curate their profile and curriculum vitae in which they describe their skills, interests and previous career steps. Recommending jobs to users is however a non-trivial task for which pure content-based features that would just match the aforementioned properties are not sufficient. For example, we often observe that there is a gap between what people specify in their profiles and what they are actually interested in. Moreover, profile and CV typically describe the past and current situation of a user but do not reflect enough the actual demands that users have with respect to their next career step. Therefore, it is crucial to also analyze the behavior of the users and exploit interaction data such as search queries, clicks on jobs, bookmarks, clicks that similar users performed, etc. Our job recommendation system exploits various features in order to estimate whether a job posting is relevant for a user or not. Some of these features rather reflect social aspects (e.g. does the user have contacts that are living in the city in which the job is offered?) while others capture to what extent the user fulfills the requirements of the role that is described in the job advertisement (e.g. similarity of user's skills and required skills). To better understand appropriate next career steps, we mine the CVs of the users and learn association rules that describe the typical career paths. This information is also made publicly available via FutureMe - a tool that allows people to explore possible career opportunities and identify professions that may be interesting for them to work in. One of the challenges when developing the job recommendation system is to collect explicit feedback and thus understanding (i) whether a recommended job was relevant for a user and (ii) whether the user was a good candidate for the job. We thus started to stronger involve users in providing feedback and build a feedback cycle that allows the recommender system to automatically adapt to the feedback that the crowd of users is providing. By displaying explanations about why certain items were suggested, we furthermore aim to increase transparency of how the recommender system works.