Before revision:  However, it is unclear whether these sampling strategies are the best choice for candidate generation in recommender system, where we face severe selection bias in the training data with an extremely large candidate set and rich features of various types .
After revision:  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 training data with an extremely large candidate set and rich features of various types .