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  1. Proceedings of the Recommender Systems Challenge (RecSys Challenge '16)
  2. An ensemble method for job recommender systems
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A preliminary study on a recommender system for the job recommendation challenge
An ensemble method for job recommender systems
Jobandtalent at RecSys Challenge 2016
A bottom-up approach to job recommendation system
A scalable, high-performance Algorithm for hybrid job recommendations
Job recommendation based on factorization machine and topic modelling
Temporal learning and sequence modeling for a job recommender system
Multi-stack ensemble for job recommendation
A combination of simple models by forward predictor selection for job recommendation
RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting
Job recommendation with Hawkes process: an effective solution for RecSys Challenge 2016

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An ensemble method for job recommender systems

Content Provider ACM Digital Library
Author Zhang, Chenrui Cheng, Xueqi
Abstract In this paper, we present an ensemble method for job recommendation to ACM RecSys Challenge 2016. Given a user, the goal of a job recommendation system is to predict those job postings that are likely to be relevant to the $user^{1}.$ Firstly, we analyze the train dataset and find several interesting patterns. Secondly, we describe our solution, which is an ensemble of two filters, combining the merits of traditional collaborative filtering and content-based filtering. Our approach finally achieved a score of 1632828.82, ranked at the 10th place on the public leaderboard.
Starting Page 1
Ending Page 4
Page Count 4
File Format PDF
ISBN 9781450348010
DOI 10.1145/2987538.2987545
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-09-15
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Word2vec Lsi Ensemble Top-n recommendation Recsys challenge 2016
Content Type Text
Resource Type Article
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