论文标题
Autorec：自动推荐系统
AutoRec: An Automated Recommender System
论文作者
论文摘要
通常需要使用现实的推荐系统来适应不断变化的数据和任务或系统地探索不同的模型。为了满足需求，我们提出了Autorec，这是一个开源自动化机器学习（AUTOML）平台，从张力流生态系统延伸，据我们所知，它是利用自动搜索和超级参数调谐的第一个框架。 Autorec还支持一条高度灵活的管道，该管道可容纳稀疏和致密输入，评级预测和点击率（CTR）预测任务以及一系列建议模型。最后，Autorec提供了一种简单，用户友好的API。在基准数据集上进行的实验揭示了autorec是可靠的，并且可以识别与最佳模型无知的模型。
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.
