In this paper, we propose a real-time online shopper behavior analysis system consisting of
two modules which simultaneously predicts the visitor's shopping intent and Web site …

The aim of this article is to determine whether the websites belonging to agri-food
cooperatives facilitate effective communication in an online environment and are adapted to …

Having a good understanding of users' interests has become increasingly important for
online retailers hoping to create a personalized service for a target market. Generally …

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning.
In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will …

Clustering is an important and popular technique in data mining. It partitions a set of objects
in such a manner that objects in the same clusters are more similar to each another than …

Heart disease is the primary cause of death nowadays. Treatments of heart disease patients
have been advanced, for example with machine-to-machine (M2M) technology to enable …

The paper addresses the problem of e-customer behavior characterization based on Web
server log data. We describe user sessions with the number of session features and aim to …

This paper presents a variant of harmony search algorithm (HS), called best–worst-mean
harmony search (BWM_HS). The main difference between the proposed algorithm and the …

Online shopping is becoming more and more common in our daily lives. Understanding
users' interests and behavior is essential to adapt e-commerce Web sites to customers' …

Research into session-based recommendation systems (SBSR) has attracted a lot of
attention, but each study focuses on a specific class of methods. This work examines and …