Recently, neighborhood-based collaborative filtering has been increasingly used in
personalized recommender systems. However, inevitably, the neighborhood selection is …

Abstract Model-based approaches to recommendation can recommend items with a very
high level of accuracy. Unfortunately, even when the model embeds content-based …

Recommender system (RS) can produce personalized service to users by analyzing their
historical information. User-based collaborative filtering (UBCF) approach is widely utilized …

One of the most commonly used techniques in the recommendation framework is
collaborative filtering (CF). It performs better with sufficient records of user rating but is not …

In this paper, a general framework of user-based collaborative filtering (CF) is developed
with a new p-moment-based similarity measure. The p-moment-based statistics (PMS) of …

The application of recommendation technologies has been crucial in the promotion of
physical and digital content across numerous global platforms such as Amazon, Apple, and …

Over the years, model-based approaches have shown their effectiveness in computing
recommendation lists in different domains and settings. By relying on the computation of …

Recently, some attempts have been made in introducing deep neural networks (DNNs) to
recommender systems for generating more accurate prediction due to the nonlinear …

Individual mobility prediction holds significant importance in urban computing, supporting
various applications such as place recommendations. Current studies primarily focus on …

The expansion of the Internet has resulted in a change in the flow of information. With the
vast amount of digital information generated online, it is easy for users to feel overwhelmed …