Recommender systems have become an important research field since the emergence of
the first paper on collaborative filtering in the mid-1990s. Although academic research on …

Purpose–In light of the growth of internet usage and its important role in the field of e‐
commerce, electronic word‐of‐mouth (eWOM) has been changing people's behavior and …

This research empirically examines the effect of various Internet shopping site qualities on
the utilitarian and hedonic values of Internet shopping. The influence of the perceived level …

Basket analytics is a powerful tool in the retail context for acquiring knowledge about
consumer shopping habits and preferences. In this paper, we propose a business analytics …

Data mining is the process which is used to analyze the large database to find the useful
pattern. Data mining can be used to learn about student's behavior from data collected using …

The Internet is emerging as a new marketing channel, so understanding the characteristics
of online customers' needs and expectations is considered a prerequisite for activating the …

Collaborative filtering (CF)-based recommender systems represent a promising solution for
the rapidly growing mobile music market. However, in the mobile Web environment, a …

The rapid growth of e-commerce has caused product overload where customers on the Web
are no longer able to effectively choose the products they are exposed to. To overcome the …

Erlearning environments are mainly based on a range of delivery and interactive services.
Web-based personalized learning recommender systems can, as a kind of services in e …

Provision of personalized recommendations to users requires accurate modeling of their
interests and needs. This work proposes a general framework and specific methodologies …