Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization

被引：10

作者：

Anand, Rohan ^{[1
]}

Beel, Joeran ^{[1
]}

机构：

[1] Trinity Coll Dublin, Dublin, Ireland

来源：

RECSYS 2020: 14TH ACM CONFERENCE ON RECOMMENDER SYSTEMS | 2020年

关键词：

AutoRecSys; AutoML; algorithm selection; hyperparameter optimization;

D O I：

10.1145/3383313.3411467

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce Auto-Surprise(1), an automated recommender system library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to an out-of-the-box Surprise library, without hyper parameter optimization, AutoSurprise performs better, when evaluated with MovieLens, Book Crossing and Jester datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise's grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.

引用

页码：585 / 587

页数：3

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