Résumé de section

      • Speaker:
        • Prof. Elisabeth Lex
        • Institute of Interactive Systems and Data Science, TU Graz, Austria
      • Title:
        • Recent Advances in (Session-based) Job Recommender Systems
      • Abstract:
        • People increasingly use business-oriented social networks such as LinkedIn or XING to attract recruiters and to look for jobs. Users of such networks make an effort to create personal profiles that best describe their skills, interests, and previous work experience. Even with such carefully structured content, it remains a non-trivial task to find relevant jobs. As a consequence, the field of job recommender systems has gained much traction in academia and the  industry. The main challenge that job recommender systems tackle is to retrieve a list of jobs for a user based on her preferences or to generate a list of potential candidates for recruiters based on the job's requirements. Besides, most online job portals offer the option to browse the available jobs anonymously in order to attract users to the portal. As a consequence, the only data a recommender system can exploit are anonymous user interactions with job postings during a session. In this talk, we will discuss ongoing research on job recommender systems. In particular, the use of neural autoencoders will be introduced to infer latent session representations in the form of embeddings, which are used to generate recommendations in a k-nearest-neighbor manner. It will be shown that autoencoders produce more novel and surprising recommendations compared to state-of-the-art baselines in session-based recommender systems.

    • Slides Lex Fichier
    • Assignment Lecture 6 Devoir