To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

The cross-domain recommendation technique is an effective way of alleviating the data
sparse issue in recommender systems by leveraging the knowledge from relevant domains …

Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have
been proposed to improve the recommendation accuracy in a target dataset …

The flow field under different flow conditions contains abundant structure information and is
of great significance for aerodynamic analysis and aircraft design. Deep learning (DL) …

Traditional recommender systems rely on user feedback such as ratings or clicks to the
items, to analyze the user interest and provide personalized recommendations. However …

We investigate meta-learning procedures in the setting of stochastic linear bandits tasks.
The goal is to select a learning algorithm which works well on average over a class of …

Data sparsity and data imbalance are practical and challenging issues in cross-domain
recommender systems (RSs). This paper addresses those problems by leveraging the …

Collaborative Filtering (CF) is the key technique for recommender systems. CF exploits user-
item behavior interactions (eg, clicks) only and hence suffers from the data sparsity issue …