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.

中文翻译：

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AutoRec：自动推荐系统

现实推荐系统通常需要适应不断变化的数据和任务或系统地探索不同的模型。为了满足这一需求，我们推出了 AutoRec，这是一个从 TensorFlow 生态系统扩展而来的开源自动机器学习 (AutoML) 平台，据我们所知，它是第一个利用 AutoML 在深度推荐模型中进行模型搜索和超参数调整的框架。AutoRec 还支持高度灵活的管道，可容纳稀疏和密集输入、评级预测和点击率 (CTR) 预测任务以及一系列推荐模型。最后，AutoRec 提供了一个简单的、用户友好的 API。在基准数据集上进行的实验表明 AutoRec 是可靠的，并且可以在没有先验知识的情况下识别与最佳模型相似的模型。