  A common challenge in personalized user preference prediction is the
cold-start problem. Due to the lack of user-item interactions, directly
learning from the new users' log data causes serious over-fitting problem.
Recently, many existing studies regard the cold-start personalized preference
prediction as a few-shot learning problem, where each user is the task and
recommended items are the classes, and the gradient-based meta learning method
(MAML) is leveraged to address this challenge. However, in real-world
application, the users are not uniformly distributed (i.e., different users may
have different browsing history, recommended items, and user profiles. We
define the major users as the users in the groups with large numbers of users
sharing similar user information, and other users are the minor users),
existing MAML approaches tend to fit the major users and ignore the minor
users. To address this cold-start task-overfitting problem, we propose a novel
personalized adaptive meta learning approach to consider both the major and the
minor users with three key contributions: 1) We are the first to present a
personalized adaptive learning rate meta-learning approach to improve the
performance of MAML by focusing on both the major and minor users. 2) To
provide better personalized learning rates for each user, we introduce a
similarity-based method to find similar users as a reference and a tree-based
method to store users' features for fast search. 3) To reduce the memory usage,
we design a memory agnostic regularizer to further reduce the space complexity
to constant while maintain the performance. Experiments on MovieLens,
BookCrossing, and real-world production datasets reveal that our method
outperforms the state-of-the-art methods dramatically for both the minor and
major users.
