Track:Web Mining

This track is part of C³S²E'2013.

 

The Web has become an indispensable instrument in the daily life for business activities, learning, entertainment, communication, etc. The offer of products and services for Internet users is practically unlimited, nevertheless, this apparent advantage is also a great drawback due to the fact that the Web provides from multiple sources a great quantity of heterogeneous information difficult to handle and interpret. In this context, data mining methods arise as efficient tools for helping users in the recovery of suitable information, products or services from the Web. The adaptation of data mining techniques to manage web data in order to discover and to extract information automatically from the Web has given rise to a new discipline, Web Mining. Web Mining includes several techniques such as text classification, multimedia mining, structure mining, learning navigation patterns, user profiling, web personalization and so on. They allow to improve information retrieval and web pages' structure, personalize the sites or recommend products or services to users.

 

Recommender systems are becoming very popular in recent years, mainly in the e-commerce sites, although they are increasing in importance in other areas such as e-learning, tourism, news pages, etc. These systems are endowed with intelligent mechanisms to personalize recommendations about products or services. Although many procedures can be used for making recommendations, those based on web mining are between the most efficient. Recently, web mining methods are being successfully used in combination with semantic web techniques. This new approach, named as Semantic Web Mining, is providing very good results in the treatment of usual problems of recommender systems.

 

This track aims at providing a forum for the presentation and discussion of the advances achieved in the web mining field. Topics of interest may include, but are not limited to, the following matters:

 

Web Usage Mining, Web Content Mining, Web Structure Mining, Text mining, Multimedia mining, Recommender Systems’ Technologies and Methodologies, User Profiles, Preference Prediction, Recommender Agents, Fuzzy Methods, Hybrid Techniques, Semantic Web Technologies for Recommender Systems, Semantic Web Mining, Semantic Web Personalization, Visualization Techniques, Applications: e-Commerce, e-Learning, Tourism Web Systems, Digital Libraries