A Movie E-shop Recommendation Model Based on Web Usage and Ontological Data

A Movie E-shop Recommendation Model Based on Web Usage and Ontological Data

Andreas Aresti (University of Patras, Greece), Penelope Markellou (University of Patras, Greece), Ioanna Mousourouli (University of Patras, Greece), Spiros Sirmakessis (Technological Education Institute of Messolonghi, Greece) and Athanasios Tsakalidis (University of Patras, Greece)
Copyright: © 2007 |Pages: 18
DOI: 10.4018/jeco.2007070102
OnDemand PDF Download:
No Current Special Offers


Recommendation systems are special personalization tools that help users to find interesting information and services in complex online shops. Even though today’s e-commerce environments have drastically evolved and now incorporate techniques from other domains and application areas such as Web mining, semantics, arti- ficial intelligence, user modeling, and profiling setting up a successful recommendation system is not a trivial or straightforward task. This paper argues that by monitoring, analyzing, and understanding the behavior of customers, their demographics, opinions, preferences, and history, as well as taking into consideration the specific e-shop ontology and by applying Web mining techniques, the effectiveness of produced recommendations can be significantly improved. In this way, the e-shop may upgrade users’ interaction, increase its usability, convert users to buyers, retain current customers, and establish long-term and loyal one-to-one relationships.

Complete Article List

Search this Journal:
Open Access Articles
Volume 19: 4 Issues (2021): 1 Released, 3 Forthcoming
Volume 18: 4 Issues (2020)
Volume 17: 4 Issues (2019)
Volume 16: 4 Issues (2018)
Volume 15: 4 Issues (2017)
Volume 14: 4 Issues (2016)
Volume 13: 4 Issues (2015)
Volume 12: 4 Issues (2014)
Volume 11: 4 Issues (2013)
Volume 10: 4 Issues (2012)
Volume 9: 4 Issues (2011)
Volume 8: 4 Issues (2010)
Volume 7: 4 Issues (2009)
Volume 6: 4 Issues (2008)
Volume 5: 4 Issues (2007)
Volume 4: 4 Issues (2006)
Volume 3: 4 Issues (2005)
Volume 2: 4 Issues (2004)
Volume 1: 4 Issues (2003)
View Complete Journal Contents Listing