Recommendation systems play an indispensable role in tourists' decision-making process.
An important issue for tourists concerns the selection of accommodation in accordance with …

Personalized recommender systems rely on knowledge of user preferences to produce
recommendations. While those preferences are often obtained from past user interactions …

The diversity of the item list suggested by recommender systems has been proven to impact
user satisfaction significantly. Most of the existing diversity optimization approaches re-rank …

The diversified recommendation of recommender systems enriches user experiences by
diversifying recommendation lists. However, the conventional post-processing strategy …

Personalization is a prominent process in today's recommender systems (RS) that enhances
user satisfaction and platform profitability. However, recent studies suggest that over …

The cold start problem in recommender systems (RSs) makes the recommendation of high-
quality content to new users difficult. While preference elicitation (PE) can be used to …

Graph neural network (GNN) is a robust model for processing non-Euclidean data, such as
graphs, by extracting structural information and learning high-level representations. GNN …

Collaborative Filtering remains the most widely used recommendation algorithm due to its
simplicity and effectiveness. However, most studies addressing the trade-off between …

Recently, the interest of graph representation learning has been rapidly increasing in
recommender systems. However, most existing studies have focused on improving …

Recommender systems have been adopted in most modern online platforms to guide users
in finding more suitable items that match their interests. Previous studies showed that …