论文标题
最小化无意识的提及：推荐使用最少必要的用户评论
Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews
论文作者
论文摘要
最近，研究人员将注意力转移到了仅使用最少必要数据的推荐系统上。推荐系统应使用不需要比所需的用户交互更多的想法来告知这种趋势，以便为用户提供有用的建议。在该职位论文中，我们为应用最少的数据的想法来推荐使用用户评论的系统。我们认为，单个用户评论的内容应受到最小化。具体而言，应自动编辑用作用于帮助用户决定购买或消费的培训数据的评论或用于帮助用户决定购买或消费的评论，以仅包含所需的信息。
Recently, researchers have turned their attention to recommender systems that use only minimal necessary data. This trend is informed by the idea that recommender systems should use no more user interactions than are needed in order to provide users with useful recommendations. In this position paper, we make the case for applying the idea of minimal necessary data to recommender systems that use user reviews. We argue that the content of individual user reviews should be subject to minimization. Specifically, reviews used as training data to generate recommendations or reviews used to help users decide on purchases or consumption should be automatically edited to contain only the information that is needed.
