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

Vasily Leksin, Andrey Ostapets, Mikhail Kamenshikov, Dmitry Khodakov and Vasily Rubtsov

MPRA Paper from University Library of Munich, Germany

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.

Keywords: recommender system; cold-start problem; Local Collective Embeddings (search for similar items in EconPapers)
JEL-codes: J28 J62 J64 (search for similar items in EconPapers)
Date: 2017-09-17
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Published in CEUR Workshop Proceeding Experimental Economics and Machine Learning.1968(2017): pp. 9-17

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:82808

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