论文标题
自动惊奇：带有Parzens估算器树（TPE）优化的自动推荐系统（Autorecsys）库
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
论文作者
论文摘要
我们介绍了自动化系统库自动惊奇。自动惊奇是惊喜推荐系统库的扩展，并简化了算法选择和配置过程。与开箱即用的惊喜库相比，自动惊喜在使用Movielens，Book Crossing和Jester Dataset评估时表现更好。它也可能导致选择运行时较低的算法。与惊奇的网格搜索相比，自动惊喜在RMSE方面的表现同样好或稍好，并且在找到最佳的超参数方面的表现较快。
We introduce Auto-Surprise, 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 out-of-the-box Surprise library, Auto-Surprise 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.
