International Workshop on Model Selection and Parameter Tuning in Recommender Systems

被引：1

作者：

Sivrikaya, Fikret ^{[1
]}

Albayrak, Sahin ^{[2
]}

Lian, Defu ^{[3
]}

机构：

[1] GT ARC Gemeinnutzige GmbH, Berlin, Germany

[2] Tech Univ Berlin, Berlin, Germany

[3] Univ Sci & Technol China, Hefei, Peoples R China

来源：

PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) | 2019年

关键词：

recommender systems; model selection; parameter tuning; hyper-parameter optimization; machine learning;

D O I：

10.1145/3357384.3358804

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Recommender systems have strongly attracted the attention of the machine learning research community with prosperous real-life deployments in the last few decades. The performance and success of most applications developed in this domain highly depend on an elaborate selection of models and configuration of their hyperparameters. The international MoST-Rec 2019 workshop addresses the issues of algorithm selection and parameter tuning for recommender systems. The workshop aims to bring together researchers from the model selection and hyperparameter tuning community in the general scope of machine learning with researchers from the recommender systems community for discussing and exchanging recent advances and open challenges in the field.

引用

页码：2999 / 3000

页数：2