Jobandtalent at RecSys Challenge 2016

Jose Ignacio Honrado, Oscar Huarte, Cesar Jimenez, Sebastian Ortega, José R. Pérez-Agüera, Joaquín Pérez-Iglesias, Álvaro Polo, Gabriel Rodríguez
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引用次数: 5

Abstract

In this paper we describe the system built by the Jobandtalent Recommendation Team to compete in the RecSys Challenge 2016. The task consisted in predicting future interactions between Users and Items within the XING platform. The data provided by XING consists of users, items, plus interactions, and impressions of items showed to those users. We decided to apply a Learning to Rank approach to find the best combination of relevance features. We finally achieved the 11th position.
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在本文中,我们描述了Jobandtalent推荐团队为参加2016年RecSys挑战赛而构建的系统。这项任务包括预测XING平台内用户和项目之间未来的交互。XING提供的数据包括用户、项目、交互以及显示给这些用户的项目印象。我们决定采用学习排序方法来找到相关特征的最佳组合。我们最终获得了第11名。
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A scalable, high-performance Algorithm for hybrid job recommendations A combination of simple models by forward predictor selection for job recommendation An ensemble method for job recommender systems Job recommendation with Hawkes process: an effective solution for RecSys Challenge 2016 A preliminary study on a recommender system for the job recommendation challenge
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