Hybrid Job Recommendation System Combines TF-IDF and Sentence-BERT

There's this new job recommendation system that's pretty cool. It uses metadata to match jobs, relying on techniques like TF-IDF for matching words and Sentence-BERT for understanding context. There’s also a filtering process that adapts to the query and an optional re-ranking feature. It organizes details like job title, company, location, and more, without needing long job descriptions or user data. When they tested it on a curated LinkedIn dataset of 31,262 jobs, they measured its accuracy with a Precision at 10 metric. This approach aims to overcome the limitations of traditional keyword searches by finding relevant job listings, even if they use different terms.