Modern recommender systems learn user representations from historical interactions, which
suffer from the problem of user feature shifts, such as an income increase. Historical …

The recommendation system makes recommendations based on the preferences of the
users. These user preferences usually come from the user's basic information, item rating …

Recommender systems rely on user-item interactions to learn Collaborative Filtering (CF)
signals and easily under-recommend the cold-start items without historical interactions. To …

Deep learning (DL)-based recommender system (RS), particularly for its advances in the
recent five years, has been startling. It reshapes the architectures of traditional RSs by lifting …

The issue of user cold-start poses a long-standing challenge to recommendation systems,
due to the scarce interactions of new users. Recently, meta-learning based studies treat …

Click-through rate (CTR) prediction is a fundamental technique in recommendation and
advertising systems. Recent studies have proved that learning a unified model to serve …

Recommender systems easily face the issue of user preference shifts. User representations
will become out-of-date and lead to inappropriate recommendations if user preference has …

Learning new task-specific skills from a few trials is a fundamental challenge for artificial
intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning …

User cold-start recommendation is a serious problem that limits the performance of
recommender systems (RSs). Recent studies have focused on treating this issue as a few …

A large-scale recommender system usually consists of recall and ranking modules. The goal
of ranking modules (aka rankers) is to elaborately discriminate users' preference on item …