@article {WALLACE:2003:1098-3058:181,
title = "INTELLIGENT ONE-STOP-SHOP TRAVEL RECOMMENDATIONS USING AN ADAPTIVE NEURAL NETWORK AND CLUSTERING OF HISTORY",
journal = "Information Technology & Tourism",
parent_itemid = "infobike://cog/itt",
publishercode ="cog",
year = "2003",
volume = "6",
number = "3",
publication date ="2003-01-01T00:00:00",
pages = "181-193",
itemtype = "ARTICLE",
issn = "1098-3058",
url = "https://www.ingentaconnect.com/content/cog/itt/2003/00000006/00000003/art00004",
doi = "doi:10.3727/1098305031436971",
keyword = "Hierarchical clustering, Linear adaptation, Recommender systems, Collaborative user modeling, Neural network, Travel planning",
author = "WALLACE, MANOLIS and MAGLOGIANNIS, ILIAS and KARPOUZIS, KOSTAS and KORMENTZAS, GEORGE and KOLLIAS, STEFANOS",
abstract = "The rapid growth of e-commerce during the last years has obliged a significant number of companies and professionals from diverse fields to turn to the Internet as a medium through which they aim to promote their products and services. A main issue for product and service providers is that, as this new market is characterized by the lack of personal contact, it is difficult to offer personalized services to end users; it is this type of service that end users look for and remain faithful to. Recommender systems belong to a new breed of software that aims to fill this gap; they rely on the analysis of past user actions to estimate the optimal way with which to interact with each user. In this article we explain why existing recommender systems are not adequate to provide for efficient personalization of interaction in the area of travel services, as they cannot support the user in all the phases of travel planning, and propose a new scheme to overcome the identified difficulties. Our approach considers the relation between different types of services in the usage history of the system. It is based on a hierarchical clustering of usage history to extract meaningful usage patterns, as well as an adaptive neural network structure that allows for online adaptation to the user, and enables the offering of intelligent recommendations.",
}