The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …

Among different recommendation techniques, collaborative filtering usually suffer from
limited performance due to the sparsity of user-item interactions. To address the issues …

Online news recommender systems aim to address the information explosion of news and
make personalized recommendation for users. In general, news language is highly …

Deep learning techniques have become the method of choice for researchers working on
algorithmic aspects of recommender systems. With the strongly increased interest in …

The rapid advancement of artificial intelligence (AI) offers exciting opportunities for
marketing practice and academic research. In this study, through the application of natural …

Collaborative filtering (CF) is a successful approach commonly used by many recommender
systems. Conventional CF-based methods use the ratings given to items by users as the …

A large amount of information exists in reviews written by users. This source of information
has been ignored by most of the current recommender systems while it can potentially …

In order to recommend products to users we must ultimately predict how a user will respond
to a new product. To do so we must uncover the implicit tastes of each user as well as the …