Cold Start problems in recommender systems pose various challenges in the adoption and
use of recommender systems, especially for new item uptake and new user engagement …

With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …

In recent years, neural models have been repeatedly touted to exhibit state-of-the-art
performance in recommendation. Nevertheless, multiple recent studies have revealed that …

In areas of machine learning such as cognitive modeling or recommendation, user feedback
is usually context-dependent. For instance, a website might provide a user with a set of …

The recommendation system makes recommendations based on the preferences of the
users. These user preferences usually come from the user's basic information, item rating …

Recommender systems are crucial in today's digital world, by enhancing user engagement
experience in digital ecosystems. Internet of things (IoT) have huge potential to generate …

Embedding-based recommender systems rely on historical interactions to model users,
which poses challenges for recommending to new users, known as the user cold-start …

Recommender systems have become fundamental building blocks of modern online
products and services, and have a substantial impact on user experience. In the past few …

In today's digital era, the abundance of online services presents users with a daunting array
of choices, spanning from streaming platforms to e-commerce websites, leading to decision …

Cross-domain data analysis is playing an increasingly important role in media convergence
and can be adopted for many applications. Most existing methods consider the domain …