AutoRec: An Automated Recommender System
- URL: http://arxiv.org/abs/2007.07224v1
- Date: Fri, 26 Jun 2020 17:04:53 GMT
- Title: AutoRec: An Automated Recommender System
- Authors: Ting-Hsiang Wang, Qingquan Song, Xiaotian Han, Zirui Liu, Haifeng Jin,
Xia Hu
- Abstract summary: We present AutoRec, an open-source automated machine learning (AutoML) platform extended from the ecosystem.
AutoRec supports a highly flexible pipeline that accommodates both sparse and dense inputs.
Experiments conducted on the benchmark datasets reveal AutoRec is reliable and can identify models which resemble the best model without prior knowledge.
- Score: 44.11798716678736
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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