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Combination of Content-Based User Profiling and Local Collective Embeddings for Job Recommendation

Author

Listed:
  • Leksin, Vasily
  • Ostapets, Andrey
  • Kamenshikov, Mikhail
  • Khodakov, Dmitry
  • Rubtsov, Vasily

Abstract

We present the approach to the RecSys Challenge 2017, which ranked 7th. The goal of the competition was to prepare job recommendations for the users of the social network for business Xing.com. Our algorithm consists of two di erent models: Content-based User Profiling and Local Collective Embeddings. The first content-based model contains many hand-tuned parameters and data insights, so it performs fairly well on the task of the challenge despite its simplicity. The second model is based on Matrix Factorization and may be applicable to a wide range of cold-start recommendation tasks. The combination of these two models have shown the best performance on local validation.

Suggested Citation

  • Leksin, Vasily & Ostapets, Andrey & Kamenshikov, Mikhail & Khodakov, Dmitry & Rubtsov, Vasily, 2017. "Combination of Content-Based User Profiling and Local Collective Embeddings for Job Recommendation," MPRA Paper 82808, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:82808
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    More about this item

    Keywords

    recommender system; cold-start problem; Local Collective Embeddings;
    All these keywords.

    JEL classification:

    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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