 However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
<clarity>  However, live recommender systems face severe unfairness of exposure with a corpus several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .
 However, live recommender systems face severe unfairness of exposure with a vocabulary several orders of magnitude larger than that of natural language, which implies that (1) MLE will preserve and even exacerbate the exposure bias in the long run , as it aims to faithfully fit the history records , and (2) suboptimal sampling and inadequate use of item features can lead to inferior representations for the items that are unfairly ignored .