With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Session-based recommendation, which aims to predict the user's immediate next action
based on anonymous sessions, is a key task in many online services (eg, e-commerce …

Attention Model has now become an important concept in neural networks that has been
researched within diverse application domains. This survey provides a structured and …

Much of the recent progress in sequential and session-based recommendation has been
driven by improvements in model architecture and pretraining techniques originating in the …

Click-through rate (CTR) prediction, whose goal is to estimate the probability of a user
clicking on the item, has become one of the core tasks in the advertising system. For CTR …

Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …

User behavior data in recommender systems are driven by the complex interactions of many
latent factors behind the users' decision making processes. The factors are highly entangled …

To learn a sequential recommender, the existing methods typically adopt the sequence-to-
item (seq2item) training strategy, which supervises a sequence model with a user's next …

Recently, neural networks have been widely used in e-commerce recommender systems,
owing to the rapid development of deep learning. We formalize the recommender system as …