Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …

In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item
interactions are entirely unavailable. The well-established, dominating identity (ID)-based …

Cross-domain rotating machinery fault diagnosis has achieved great success recently with
the development of deep transfer learning. However, conventional deep transfer learning …

Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble
problem when users suffer the familiar, repeated, and even predictable recommendations …

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 …

Learnersourcing offers great potential for scalable education through student content
creation. However, predicting student performance on learnersourced questions, which is …

Recommendation systems provide personalized service to users and aim at suggesting to
them items that they may prefer. There is an increasing requirement of next-item …

Deep neural network based recommendation systems have achieved great success as
information filtering techniques in recent years. However, since model training from scratch …

Although pretraining has garnered significant attention and popularity in recent years, its
application in graph-based recommender systems is relatively limited. It is challenging to …