Automating the Extraction of Static Content and Dynamic Behaviour from e-Commerce Websites

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Dias, JP (Author) FEUP; Hugo Sereno Ferreira (Author) FEUP

Conference proceedings International

Title: 8TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT-2017) AND THE 7TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT 2017)

Pages: 297-304

8th International Conference on Ambient Systems, Networks and Technologies (ANT) / 7th International Conference on Sustainable Energy Information Technology (SEIT)

Madeira, PORTUGAL, MAY 16-19, 2017

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00M-V97

DOI: 10.1016/j.procs.2017.05.355

Abstract (EN): E-commerce website owners rely heavily on analysing and summarising the behaviour of costumers, making efforts to influence user actions and optimize success metrics. Machine learning and data mining techniques have been applied in this field, greatly influencing the Internet marketing activities. When faced with a new e-commerce website, the data scientist starts a process of collecting real-time and historical data about it, analysing and transforming this data in order to get a grasp into the website and its users. Data scientists commonly resort to tracking domain-specific events, requiring code modification of the web pages. This paper proposes an alternative approach to retrieve information from a given e-commerce website, collecting data from the site's structure, retrieving semantic information in predefined locations and analysing user's access logs, thus enabling the development of accurate models for predicting users' future behaviour. This is accomplished by the application of a web mining process, comprehending the site's structure, content and usage in a pipeline, resulting in a web graph of the website, complemented with a categorization of each page and the website's archetypical user profiles. 1877-0509 (C) 2017 The Authors. Published by Elsevier B.V.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8