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

Leksin, Vasily and Ostapets, Andrey and Kamenshikov, Mikhail and Khodakov, Dmitry and Rubtsov, Vasily (2017): Combination of Content-Based User Profiling and Local Collective Embeddings for Job Recommendation. Published in: CEUR Workshop Proceeding , Vol. 1968, No. Experimental Economics and Machine Learning (28 October 2017): pp. 9-17.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: recommender system; cold-start problem; Local Collective Embeddings
• Subjects: J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J28 - Safety ; Job Satisfaction ; Related Public Policy; J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers > J62 - Job, Occupational, and Intergenerational Mobility; J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers > J64 - Unemployment: Models, Duration, Incidence, and Job Search

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