With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

First derived from human intuition, later adapted to machine translation for automatic token
alignment, attention mechanism, a simple method that can be used for encoding sequence …

Commercial e-learning platforms have to overcome the challenge of resource overload and
find the most suitable material for educators using a recommendation system (RS) when an …

Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the
cold-start problem, which has a significantly negative impact on users' experiences with …

Large language models (LLMs) are believed to contain vast knowledge. Many works have
extended LLMs to multimodal models and applied them to various multimodal downstream …

Personalized recommender systems have become indispensable in today's online world.
Most of today's recommendation algorithms are data-driven and based on behavioral data …

Modern deep learning-based recommendation systems exploit hundreds to thousands of
different categorical features, each with millions of different categories ranging from clicks to …

With the spread of COVID-19, the “untact” culture in South Korea is expanding and
customers are increasingly seeking for online services. A recommendation system serves as …

Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation
accuracy and ease in online penalization and thus is favored by the industrial recommender …