论文标题
基于自动编码的一级协作过滤的噪声对比估计
Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering
论文作者
论文摘要
单级协作过滤（OC-CF）是一个常见的建议问题类别，其中仅观察到正类别（例如，购买，点击）。基于AutoCoder的推荐人（例如Autorec和变体）在许多OC-CF基准上都表现出强劲的性能，但从经验上也遭受了强烈的流行偏见。虽然OC-CF设置中的负面样品的仔细选择可以减轻流行性偏见，但负面抽样（NS）通常比最终任务本身更好。为了解决这个问题，我们提出了一个两头自动的Autorec，首先使用一个负面采样，然后通过一个头部通过一个头训练嵌入层，然后通过第二个头训练最终任务。尽管此NS-Autorec改善了Autorec的结果，并且在OC-CF问题上胜过许多最先进的基线，但我们注意到负面抽样仍然可能需要大量时间进行训练。由于已知负采样是噪声对比估计（NCE）的特殊情况，因此我们适应了最近提出的封闭形式的NCE解决方案，用于协作过滤到autorec，以产生NCE-Autorec。总体而言，我们表明，与多个现实世界中的最先进的推荐人相比，我们的小说两头Autorec车型（NCE-Autorec和NS-Autorec）成功缓解了受欢迎程度的偏见问题并保持竞争性绩效。
One-class collaborative filtering (OC-CF) is a common class of recommendation problem where only the positive class is explicitly observed (e.g., purchases, clicks). Autoencoder based recommenders such as AutoRec and variants demonstrate strong performance on many OC-CF benchmarks, but also empirically suffer from a strong popularity bias. While a careful choice of negative samples in the OC-CF setting can mitigate popularity bias, Negative Sampling (NS) is often better for training embeddings than for the end task itself. To address this, we propose a two-headed AutoRec to first train an embedding layer via one head using Negative Sampling then to train for the final task via the second head. While this NS-AutoRec improves results for AutoRec and outperforms many state-of-the-art baselines on OC-CF problems, we notice that Negative Sampling can still take a large amount of time to train. Since Negative Sampling is known to be a special case of Noise Contrastive Estimation (NCE), we adapt a recently proposed closed-form NCE solution for collaborative filtering to AutoRec yielding NCE-AutoRec. Overall, we show that our novel two-headed AutoRec models (NCE-AutoRec and NS-AutoRec) successfully mitigate the popularity bias issue and maintain competitive performance in comparison to state-of-the-art recommenders on multiple real-world datasets.
