The personalized recommendation system influences the recommendation of ideological
and political teaching resources in universities, resulting in a high MAE score. As a result …

A commercially viable multi-stakeholder recommendation system maximizes the utility gain
by learning the personalized preferences of multiple stakeholders, such as consumers and …

Previous knowledge graph-based recommendation models use Graph Neural Networks to
aggregate information from neighbors. However, entities of the same hop should have …

Serendipity is a critical factor in the Recommender Systems (RS) in delivering pleasantly
surprising, novel, yet contextually relevant recommendations. Most existing methods …

Recommender systems have attracted numerous scholars' attention. Group recommender is
a fashionable though challenging topic in the field of recommender systems. Most methods …

Among the model-based collaborative filtering (CF) recommendation algorithms, matrix
factorization (MF) technology is quite efficient. An ever-increasing focus has witnessed that …

Abstract Human Image Generation is an important information processing technique.
Producing realistic-looking texture is crucial for Generative Adversarial Networks (GAN) …

The commercially applicable Recommendation system (RS) exploits multi-criteria rating-
based user-item interaction to learn and personalize user preferences using the Multi …

Attention mechanism has been proven to be a useful model for sequence recommendation.
Traditional multi-head self-attention architecture can exploit the entire user sequence and …

Modern society devotes a significant amount of time to digital interaction. Many of our daily
actions are carried out through digital means. This has led to the emergence of numerous …