Recommender systems have become essential in modern information systems and Internet
applications by delivering personalized and pertinent content to users. While conventional …

In recent years, recommendation systems have become essential for businesses to enhance
customer satisfaction and generate revenue in various domains, such as e-commerce and …

Traveling Salesman Problems (TSPs) have been a long-lasting interesting challenge to
researchers in different areas. The difficulty of such problems scales up further when …

Multiobjective optimization problems (MOPs) are prevalent in machine learning, with
applications in multi-task learning, learning under fairness or robustness constraints, etc …

Given an input query, a recommendation model is trained using user feedback data (eg,
click data) to output a ranked list of items. In real-world systems, besides accuracy, an …

As the problem of information overload becomes increasingly serious, traditional
recommendation algorithms are difficult to efficiently provide assistance to users. In this …

Graph convolutional networks are capable of handling non-Euclidean data with sparse
features, and some research has begun to apply them to the field of recommendation …

A multi-objective turbine shape optimization method based on deep reinforcement learning
(DRL) is proposed. DRL-based optimization methods are useful for repeating optimization …

The proliferation of video-on-demand (VOD) services has led to a paradox of choice,
overwhelming users with vast content libraries and revealing limitations in current …

Information overload has made it difficult for users to get needed data. In this context,
recommender systems are used to filter information and provide personalized …