SOTAVerified

AutoRec: An Automated Recommender System

2020-06-26Code Available0· sign in to hype

Ting-Hsiang Wang, Qingquan Song, Xiaotian Han, Zirui Liu, Haifeng Jin, Xia Hu

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

Realistic recommender systems are often required to adapt to ever-changing data and tasks or to explore different models systematically. To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models. AutoRec also supports a highly flexible pipeline that accommodates both sparse and dense inputs, rating prediction and click-through rate (CTR) prediction tasks, and an array of recommendation models. Lastly, AutoRec provides a simple, user-friendly API. Experiments conducted on the benchmark datasets reveal AutoRec is reliable and can identify models which resemble the best model without prior knowledge.

Tasks

Reproductions