本研究针对传统推荐系统中高维嵌入的不可解释性和用户控制性不足的问题，提出了TEARS方法。通过将用户兴趣编码为自然文本，TEARS增强了透明性和可编辑性，从而实现了超越三种流行变分自编码器模型的推荐效果，同时提升了用户对推荐内容的控制能力。

Even on ten-year-old hardware, it's possible to create the game of the year. Truly exciting storylines and highly creative gameplay mechanisms are far more important than improvements in graphics quality.