A scalable, high-performance Algorithm for hybrid job recommendations

Abstract

Recommender systems can be used as a tool to assist people in finding a job. However, this specific domain requires expert algorithms with domain knowledge to recommend jobs conformable to people's expertise and interests. This is the topic of the Recsys Challenge 2016, which aims for an algorithm that predicts the job postings that a user will positively interact with. Our solution is a hybrid algorithm combining a content-based and KNN approach. The content-based algorithm matches features of candidate recommendations and job postings of historical interactions. The KNN approach searches for the job postings that are the most similar to the postings the user interacted with in the past. The resulting combination is a lightweight algorithm that is fast and scalable, generating recommendations with a proper evaluation score.