  This paper proposes a recommender system to alleviate the cold-start problem
that can estimate user preferences based on only a small number of items. To
identify a user's preference in the cold state, existing recommender systems,
such as Netflix, initially provide items to a user; we call those items
evidence candidates. Recommendations are then made based on the items selected
by the user. Previous recommendation studies have two limitations: (1) the
users who consumed a few items have poor recommendations and (2) inadequate
evidence candidates are used to identify user preferences. We propose a
meta-learning-based recommender system called MeLU to overcome these two
limitations. From meta-learning, which can rapidly adopt new task with a few
examples, MeLU can estimate new user's preferences with a few consumed items.
In addition, we provide an evidence candidate selection strategy that
determines distinguishing items for customized preference estimation. We
validate MeLU with two benchmark datasets, and the proposed model reduces at
least 5.92% mean absolute error than two comparative models on the datasets. We
also conduct a user study experiment to verify the evidence selection strategy.
