当新不如旧：深度学习是否真正有益于从隐式反馈中进行推荐？（英文版）

英文标题：When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?中文摘要：通过对 13 种流行的推荐模型进行大规模实验，首次提出了一组评估策略来比较神经模型和传统模型在推荐系统的表现，发现在不同方面神经模型都不一定优于传统模型，并且在子群体方面表现更优。英文摘要：In recent years, neural models have been repeatedly touted to exhibitstate-of-the-art performance in recommendation. Nevertheless, multiple recentstudies have revealed that the reported state-of-the-art results of many neuralrecommendation models cannot be reliably replicated. A primary reason i