论文标题
贝叶斯负面抽样供推荐
Bayesian Negative Sampling for Recommendation
论文作者
论文摘要
如何从未标记的数据中采样高质量的负面实例，即负抽样，对于培训隐式协作过滤和对比度学习模型很重要。尽管以前的研究提出了一些样本信息实例的方法，但很少有用于将假阴性与无偏见的负面采样的真实负面分歧。根据我们对否定分数的订单关系分析，我们首先得出了真正的负否和假阴性的阶级有条件密度。接下来，我们设计了一个用于负分类的贝叶斯分类器，我们从中定义了一个模型 - 不合稳定的后验概率估计值为实例为真为负面的负面信号测量。我们还提出了一项贝叶斯最佳抽样规则，以采样高质量的负面因素。提出的贝叶斯阴性采样（BNS）算法具有线性时间复杂性。实验研究以更好的采样质量和更好的建议性能来验证BNS优于同龄人的优势。
How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is important for training implicit collaborative filtering and contrastive learning models. Although previous studies have proposed some approaches to sample informative instances, few has been done to discriminating false negative from true negative for unbiased negative sampling. On the basis of our order relation analysis of negatives' scores, we first derive the class conditional density of true negatives and that of false negatives. We next design a Bayesian classifier for negative classification, from which we define a model-agnostic posterior probability estimate of an instance being true negative as a quantitative negative signal measure. We also propose a Bayesian optimal sampling rule to sample high-quality negatives. The proposed Bayesian Negative Sampling (BNS) algorithm has a linear time complexity. Experimental studies validate the superiority of BNS over the peers in terms of better sampling quality and better recommendation performance.
