Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
Information Retrieval
2020-09-01 v1 Machine Learning
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2008.13532,
title = {Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization},
author = {Rohan Anand and Joeran Beel},
journal= {arXiv preprint arXiv:2008.13532},
year = {2020}
}
Comments
To be presented at RecSys '20 Fourteenth ACM Conference on Recommender Systems, September 21-26, 2020, Virtual Event