The personalized product preference and decision-making recommendation systems are
highly demanded to handle big data and to increase service quality of the e-commerce …

In recent years, there has been a growing interest in analyzing text data from different
scientific fields. The significant advancement of Artificial Intelligence in Natural Language …

Recommender systems aim to forecast users' rank, interests, and preferences in specific
products and recommend them to a user for purchase. Collaborative filtering is the most …

The exponential growth of online social network platforms and applications has led to a
staggering volume of user-generated textual content, including comments and reviews …

In personalized recommender systems, the collaborative filtering (CF) recommendation
approaches have been widely used to predict the preferences of users in real-world …

Most of the existing topic models are based on the Latent Dirichlet Allocation (LDA) or the
variational autoencoder (VAE), but these methods have inherent flaws. The a priori …

Recommender systems (RS) are substantial for online shopping or digital content services.
However, due to some data characteristics or insufficient historical data, may encounter …

Conversational recommender systems (CRS) aim to provide high-quality recommendations
through fewer multi-turn conversations. However, because short conversation histories lack …

The cold-start problem is one of the main challenges in recommender systems and
specifically in collaborative filtering methods. Such methods, albeit effective, typically can not …

Although several approaches have been proposed throughout the last decade to build
recommender systems (RS), most of them suffer from the cold-start problem. This problem …