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

by   Rohan Anand, et al.

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.


Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL

While early AutoML frameworks focused on optimizing traditional ML pipel...

Born for Auto-Tagging: Faster and better with new objective functions

Keyword extraction is a task of text mining. It is applied to increase s...

AutoGL: A Library for Automated Graph Learning

Recent years have witnessed an upsurge of research interests and applica...

Reinforcement-based Simultaneous Algorithm and its Hyperparameters Selection

Many algorithms for data analysis exist, especially for classification p...

Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network

The great amount of datasets generated by various data sources have pose...

Learning to Prove with Tactics

We implement a automated tactical prover TacticToe on top of the HOL4 in...

Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities

We present a new task of query auto-completion for estimating instance p...