AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, so do risks from misalignment. To provide a comprehensive …

Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

Recommending cold items in recommendation systems is a longstanding challenge due to
the inherent differences between warm items, which are recommended based on user …

Recommender systems are feedback loop systems, which often face bias problems such as
popularity bias, previous model bias and position bias. In this paper, we focus on solving the …

In recent years, explainable recommendation has been a topic of active study. This is
because the branch of the machine learning field related to methodologies is enabling …

Artificial Intelligence applications gradually move outside the safe walls of research labs and
invade our daily lives. This is also true for Machine Learning methods on Knowledge …

As a promising solution for model compression, knowledge distillation (KD) has been
applied in recommender systems (RS) to reduce inference latency. Traditional solutions first …

In recommender systems, top-N recommendation is an important task with implicit feedback
data. Although the recent success of deep learning largely pushes forward the research on …

There has been sustained interest from both academia and industry throughout the years
due to the importance and practicability of recommendation systems. However, several …

Recently, deep learning-based models have been widely studied for click-through rate
(CTR) prediction and lead to improved prediction accuracy in many industrial applications …