 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 .
<coherence>  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 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 in order 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 .